Cp7301 Software Process and Project Management Notes

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SOFTWARE PROCESS AND PROJECT MANAGEMENT MANAGEME NT

UNIT I DEVELOPMENT DEVELOPME NT LIFE CYCLE PROCESS 1.1Overview of software development life cycle There are various software development approaches defined and designed which are used/employed during development process of software, these approaches are also referred as “Software Development Process Models” (e.g. Waterfall Waterfall mod model, el, incremental incremental mo model, del, Vmodel, model, iterative iterative model, model, etc.). Each process model follows a particular life cycle in order to ensure success in process of software development. Software life cycle models describe phases of the software cycle and the order in which those phases are executed. Each phase produces deliverables required by the next phase in the life cycle. Requirements are translated into design. Code is produced according to the the design which is called development phase. After coding and development the testing verifies the deliverable of the implementation phase against requirements. There are following six phases in every Software development life cycle model: 1. Requi Requireme rement nt gather gathering ing and analy analysis sis 2. Design 3. Imp Implem lement entatio ation n or cod coding ing 4. Testing 5. De Depl ploy oym men entt 6. Ma Main inte tena nanc ncee 1) Requirement gathering and analysis: Business requirements are gathered in this phase. This phase is the main focus of the project managers and stake holders. Meetings with managers, stake holders and users are held in order to determine determine the requirements like; Who is going going to use the the system? system? How will they they use the the system? system? What data data should should be input input into the system? system? What data should should be output output by the the system? system? These are general general quest questions ions that get answered during a requirements gathering phase. After requirement gathering these requirements are analyzed for their validity and the possibility of incorporating the requirements in the system to be development is also studied. Finally, a Requirement Specification document is created which serves the purpose of guideline for the next phase of the model. 2) Design: In this phase the system and software design is prepared from the requirement specifications which were studied in the first phase. System Design helps in specifying hardware and system requirements and also helps in defining overall system architecture. The system design specifications serve as input for the next phase of the model. 3) Implementation / Coding: Coding: On receiving system design documents, the work is divided in modules/units and actual coding is started. Since, in this phase the code is produced so it is the main focus for the developer. This is the longest phase of the software development life cycle. 1

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4) Testing: Testing: After the code is developed it is tested against the requirements to make sure that the product is actually solving the needs addressed and gathered during the requirements phase. During this phase unit testing, integration testing, system testing, acceptance testing are done. 5) Deployment: After successful testing the product is delivered / deployed to the customer for their use. 6) Maintenance: Once when the customers starts using the developed system then the actual problems comes up and needs to be solved from time ti me to time. This process where the care is taken for the developed product is known as maintenance. 1.2Introduction to Process  An executing program along with all the things that the program can affect or be affected byThe dynamic execution context (“active spirit”) of a program (as opposed to the program, which is static.)  Only one thing happens at a time within a process execution and scheduli scheduling ng  The unit of execution Some systems allow only one process (mostly personal computers). They are called uniprogrammingsystems (not uniprocessing; that means only oneprocessor). Easier to write somee parts of OS, but many som many other things things are hard to do. E.g. E.g. compile compile a program in in background while you edit another file; answer your phone and take messages while you’re busy hacking. Very difficult to do anything anything network-related under uniprogramming. uniprogramming. Most systems allow more than one process. They are called multiprogramming What’s in a process? A process contains all a ll the state of a program in execution:  the code for the running programs  the data for the running program  the execution stack showing the state of all calls in progress  the program counter, indicating the next instruction  the set of CPU registers with current values  the set of OS resources held by the program (references to open files, network connections) Process State Each process has an execution state that indicates what it is currently doing  ready—waiting for the CPU  running—executing instructions on the CPU  waiting—waiting for an event, e.g., I/O completion

OS has to keep track of all the processes. Each process is represented in the OS by a data structure called process control block (PCB): 2

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queue pointers  process state  process number  program counter (PC)  stack pointer (SP)  general purpose register contents  floating point register contents  memory state  I/O state  scheduling information  accounting information How can several processes share one CPU? OS O S must make sure that processes don’t interfere with each other. This means (fa ir scheduling).  Making sure each gets a chance to run (fair  Making sure they don’t modify each other’s state (protection).  Dispatcher : inner-most portion of the OS that runs processes:  Run process for a while  Save state  Load state of another process  Run it ... 1.3Personal software process(PSP) The Personal Software Process (PSP) is a structured structured software software developme development nt process process that is intended intended to help software software engineers engineers understand understand and improve improve their performan performance, ce, by using a [ "disciplined, data-driven procedure". The PSP PSP was created created by Watts Humphrey Humphrey to apply the underlying principles principles of the Software Engineering Institute’s Institute’s (SEI)Capability Maturity Model (CMM) to the software development practices of a single developer. It claims to give software software engineers engineers the process process skills necessary necessary to work work on a Team Software Software Process (TSP) team. The PSP aims to provide software engineers with disciplined methods for improving personal software development processes. The PSP helps helps software engineers to: Improve their estimating and planning skills. Make commitments they can keep. Manage the quality of their projects. Reduce the number of defects in their work. The goal of the PSP is to help developers produce zero-defect, quality products on schedule. Low-defect and zero defect products have become the reality for some developers and TSP teams, such as the Motorola division in Florida that achieved zero defects in over 18 projects through implementing PSP techniques. 

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PSP strucure PSP training follows an evolutionary improvement approach: an engineer learning to integrate integ rate the PSP into into his or her process process begins begins at the first first level - PSP0 - and progresse progressess in process maturity to the final level - PSP2.1. Each Level has detailed scripts, checklists and templates to guide the engineer through required steps and helps the engineer improve his own personal software process. Humphrey encourages proficient engineers to customise these scripts and templates as they gain an understanding of their own strengths and weaknesses. Process The input to PSP is the requirements; requirements document is completed and delivered to the engineer. PSP0, PSP0.1 (Introduces process discipline and measurement) PSP0 has 3 phases: planning, development (design, coding, test) and a post mortem. A baseline is established of current process measuring: time spent on programming, faults injected/removed, size of a program. In a post mortem, the engineer ensures all data for the projects has been properly recorded and analysed. PSP0.1 advances the process by adding a coding standard, a size measurement and the development of a personal process improvement plan (PIP). In the PIP, the engineer records ideas for improving his own process. PSP1, PSP1.1 (Introduces estimating and planning) Based upon the baseline data collected in PSP0 and PSP0.1, the engineer estimates how large a new program will be and prepares a test report (PSP1). Accumulated data from previous projects is used to estimate the total time. Each new project will record the actual time spent. This information is used for task and schedule planning and estimation (PSP1.1). PSP2, PSP2.1 (Introduces quality management and design) PSP2 adds two new phases: design review and code review. Defect prevention and removal are the focus at the PSP2. Engineers learn to evaluate and improve their process by measuring how long tasks take and the number of defects they inject and remove in each phase of development. Engineers construct and use checklists for design and code reviews. PSP2.1 introduces design specification and analysis techniques tec hniques (PSP3 is a legacy level that has been superseded by TSP.) One of the core aspects of the PSP is using historical data to analyze and improve process performance. PSP data collection is supported by four main elements: Scripts Measures Standards Forms The PSP scripts provide expert-level guidance to following the process steps and they provide a framework for applying the PSP measures. measures. The PSP has four core measures: 

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Size – the size measure for a product product part, such as lines of code (LOC). Effort – the time required to complete complete a task, usually recorded in minutes. Quality Qualit y – the number number of defects in the product. product. Schedu Sch edule le – a measur measuree of project project progr progress ession ion,, tracke tracked d agains againstt pla planne nned d and actua actuall completion dates. Applying standards to the process can ensure the data is precise and consistent. Data is logged in forms, normally using a PSP software tool. The SEI has developed a PSP tool and there are also open source options available, such as Process Pr ocess Dashboard. The key data collected in the PSP tool are time, defect, and size data – the time spent in each phase; when and where defects were injected, found, and fixed; and the size of the product parts. Software developers use many other measures that are derived from f rom these three basic measures to understand and improve their performance. Derived measures include: estimation accuracy (size/time) prediction intervals (size/time) time in phase distribution defect injection distribution defect removal distribution productivity reuse percentage cost performance index planned value earned value predicted earned value defect density defect density by phase defect removal rate by phase defect removal leverage review rates process yield phase yield failure cost of quality (COQ) appraisal COQ appraisal/failure COQ ratio Planning and tracking Logging time, defect, and size data is an essential part of planning and tracking PSP projects, as historical data is used to improve estimating accuracy. The PSP uses the PROxy-Based PROxy-Based Estimati Estimation on (PROBE) method method to improve improve a developer’s developer’s estimating skills for more accurate project planning. For project tracking, the PSP uses the earned earned value value metho method. d. 

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The PSP also uses statistical techniques, such as correlation, linear regression, and standard deviation, to translate data into useful information for improving estimating, planning and quality. These statistical formulas are calculated by the PSP tool. Using the PSP The PSP is intended to help a developer improve their personal process; therefore PSP developers are expected to continue adapting the process to ensure it meets their personal needs. 1.4Team software process(TSP) In combinatio combination n with the the Personal Personal Software Software Process Process (PSP), the the Team Software Process (TSP) provides a defined operational process framework that is designed to help teams of managers and engineers organize projects and produce software products that range in size from small projects of several thousand lines of code (KLOC) to very large projects greater than half a million lines of code.The TSP is intended to improve the levels of quality and productivity of a team's software development project, in order to help them better meet the cost and schedule commitments of developing a software system. The initial version of the TSP was developed and piloted by Watts Humphrey in the late 1990s and the Technical Report for TSP sponsored by the U.S. Department of Defense was Introduction to the Team published in November 2000. The book by Watts Humphrey, Introduction Software Process, presents a view the TSP intended for use in academic settings, that focuses on the process of building a software production team, establishing team goals, distributing team roles, and other teamwork-related activities. How TSP Works Before engineers can participate in the TSP, it is required that they have already learned about the PSP, so that the TSP can work effectively. Training is also required for other team members, the team lead, and management.

The TSP software development development cycle begins with a planning process called the launch, led by a coach who has been specially trained, and is either certified or provisional. The launch is designed to begin the team building process, and during this time teams and managers establish goals, define team roles, assess risks, estimate effort, allocate tasks, and produce a team plan. During an execution phase, developers track planned and actual effort, schedule, and defects, meeting regularly (usually weekly) to report status and revise plans. A development cycle ends with a Post Mortem to assess performance, revise planning parameters, and capture lessons learned for process improvement. improvement. The coach role focuses on supporting the team and the individuals on the team as the process expert while being independent of direct project management responsibility. responsibility. The team leader 6

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SOFTWARE PROCESS AND PROJECT MANAGEMENT

role is different from the coach role in that, team leaders are responsible to management for products and project outcomes while the coach is responsible for developing individual and team performance. 1.5 Unifiedprocesses The Unified Software Development Process or Unified Process is a popular iterative and incremental software development process framework. The best-known and extensively documented refinement of the Unified Process is the Rational Unified Process (RUP). Other examples are OpenUP and Agile Unified Process. Overview The Unified Process is not simply a process, but rather an extensible framework which should be customized for specific organizations or projects. The Rational Unified Process is, similarly, a customizable framework. As a result it is often impossible to say whether a refinement of the process was derived from UP or from RUP, and so the names tend to be used interchangeably. The name Unified Process as opposed to Rational Unified Process is generally used to describe the generic process, including those elements which are common to most refinements. The Unified Process name is also used to avoid potential issues of trademark infringement since Rational Unified Processand RUP are trademarks of IBM. The first book to describe the process was titled The Unified Software Development Process (ISBN 0-20157169-2) and published in 1999 by Ivar Jacobson, Grady Booch and James Rumbaugh. Since then various authors unaffiliated with Rational Software have published books and articles using the name Unified Process, whereas authors affiliated with Rational Software have favored the name Rational Unified Process.

Unified Process Characteristics

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Iterative and Incremental

Diagram illustrating how the relative emphasis of different disciplines changes over the course of the projectThe Unified Process is an iterative and incremental development process. The Elaboration, Construction and Transition phases are divided into a series of timeboxed iterations. (The Inception phase may also be divided into iterations for a large project.) Each iteration results in an increment , which is a release of the system that contains added or improved functionality compared with the previous release.Although most iterations will include work in most of the process disciplines ( e.g.Requirements, Design, Implementation, Testing) the relative effort and emphasis will change over the course of the project. Use Case Driven In the Unified Process, use cases are used to capture the functional requirements and to define the contents of the iterations. Each iteration takes a set of use cases or scenarios from requirements all the way through implementation, test and deployment. Architecture Centric The Unified Process insists that architecture sit at the heart of the project team's efforts to shape the system. Since no single model is sufficient to cover all aspects of a system, the Unified Process supports multiple architectural models and views. One of the most important deliverables of the process is the executable architecture baseline which is created during the Elaboration phase. This partial implementation of the system serves to validate the architecture and act as a foundation for remaining development.

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Risk Focused The Unified Process requires the project team to focus on addressing the most critical risks early in the project life cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected in order to ensure that the greatest risks are addressed first. Project Lifecycle The Unified Process divides the project into four phases: Inception Elaboration Construction Transition 

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Inception Phase Inception is the smallest phase in the project, and ideally it should be quite short. If the Inception Phase is long then it may be an indication of excessive up-front specification, which is contrary to the spirit of the Unified Process. The following are typical goals for the Inception phase. Establish a justification or business case for the project Establish the project scope and boundary conditions Outline the use cases and key requirements that will drive the design tradeoffs Outline one or more candidate architectures Identify risks Prepare a preliminary project schedule and cost estimate The Lifecycle Objective Milestone marks the end of the Inception phase. Develop an approximate vision of the system, make the business case, define the scope, and produce rough estimate for cost and schedule. 

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Elaboration Phase During the Elaboration phase the project team is expected to capture a healthy majority of the system requirements. However, the primary goals of Elaboration are to address known risk factors and to establish and validate the system architecture. Common processes undertaken in this phase include the creation of use case diagrams, conceptual diagrams (class diagrams with only basic notation) and package diagrams (architectural diagrams). The architecture is validated primarily through the implementation of an Executable Architecture Baseline. This is a partial implementation of the system which includes the core, most architecturally significant, components. It is built in a series of small, time boxed iterations. By the end of the Elaboration phase the system architecture must have stabilized and the executable architecture baseline must demonstrate that the architecture will support 9

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the key system functionality and exhibit the right behavior in terms of performance, scalability and cost. The final Elaboration phase deliverable is a plan (including cost and schedule estimates) for the Construction phase. At this point the plan should be accurate and credible, since it should be based on the Elaboration phase experience and since significant risk factors should have been addressed during the Elaboration phase. Construction Phase Construction is the largest phase in the project. In this phase the remainder of the system is built on the foundation laid in Elaboration. System features are implemented in a series of short, timeboxed iterations. Each iteration results in an executable release of the software. It is customary to write full text use cases during the construction phase and each one becomes the start of a new iteration. Common UML (Unified Modelling Language) diagrams used during this phase include Activity, Sequence, Collaboration, State (Transition) and Interaction. Transition Phase The final project phase is Transition. In this phase the system is deployed to the target users. Feedback received from an initial release (or initial releases) may result in further refinements to be incorporated over the course of several Transition phase iterations. The Transition phase also includes system conversions and user training. Refinements and Variations Refinements of the Unified Process vary from each other in how they categorize the project disciplines or workflows. TheRational Unified Process defines nine disciplines: Business Modeling, Requirements, Analysis and Design, Implementation,Test, Deployment, Configuration and Change Management, Project Management, and Environment. The Enterprise Unified Process extends RUP through the addition of eight "enterprise" disciplines. Agile refinements of UP such as OpenUP/Basicand the Agile Unified Process simplify RUP by reducing the number of disciplines. Refinements also vary in the emphasis placed on different project artifacts. Agile refinements streamline RUP by simplifying workflows and reducing the number of expected artifacts. Refinements also vary in their specification of what happens after the Transition phase. In the Rational Unified Process the Transition phase is typically followed by a new Inception phase. In the Enterprise Unified Process the Transition phase is followed by a Production phase.

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The number of Unified Process refinements and variations is countless. Organizations utilizing the Unified Process invariably incorporate their own modifications and extensions. The following is a list of some of the better known refinements and variations. Agile Unified Process (AUP), a lightweight variation developed by Scott W. Ambler Basic Unified Process (BUP), a lightweight variation developed by IBM and a precursor to OpenUP Enterprise Unified Process (EUP), an extension of the Rational Unified Process Essential Unified Process (EssUP), a lightweight variation developed by Ivar Jacobson Open Unified Process (OpenUP), the Eclipse Process Framework software development process Rational Unified Process (RUP), the IBM / Rational Software development process Oracle Unified Method (OUM), the Oracle development and implementation process Rational Unified Process-System Engineering (RUP-SE), a version of RUP tailored by Rational Software for System Engineering 

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1.6Agile Processes In software development life cycle, there are two main considerations, one is to emphasize on process and the other is the quality of the software and process itself. Agile software processes is an iterative and incremental based development, where requirements are changeable according to customer needs. It helps in adaptive planning, iterative development and time boxing. It is a theoretical framework that promotes foreseen interactions throughout the development cycle. There are several SDLC models like spiral, waterfall, RAD which has their own advantages. SDLC is a framework that describes the activities performed at each stage of a software development life cycle.The software development activities such as planning, analysis, design, coding, testing and maintenance which need to be performed according to the demand of the customer. It depends on the various applications to choose the specific model. In this paper, however, we will study the agile processes and its methodologies. Agile process is itself a software development process.Agile process is an iterative approach in which customer satisfaction is at highest priority as the customer has direct involvement in evaluating the software.

The agile process follows the software development life cycle which includes requirements gathering, analysis, design , coding , testing and delivers partially implemented software and waits for the customer feedback. In the whole process , customer satisfaction is at highest priority with faster development time. Characteristics of agile projects Agile process requires less planning and it divides the tasks into small increments. Agile process is meant for short term projects with an effort of team work that follows the 11

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software development life cycle. Software development life cycle includes the following phases 1.Requirements gathering, 2.Analysis, 3.Design, 4.Coding , 5.Testing, 6.Maintenance. The involvement of software team management with customers reduces the risks associated with the software. This agile process is an iterative process in which changes can be made according to the customer satisfaction. In agile process new features can be added easily by using multiple iterations. 1. Iterative The main objective of agile software processes is satisfaction of customers, so it focuses on single requirement with multiple iterations. 2. Modularity Agile process decomposes the complete system into manageable pieces called modules. Modularity plays a major role in software development processes. 3. Time Boxing As agile process is iterative in nature, it requires the time limits on each module with respective cycle. 4. Parsimony In agile processes parsimony is required to mitigate risks and achieve the goals by minimal number of modules. 5. Incremental As the agile process is iterative in nature, it requires the system to be developed in increments, each increment is independent of others, and at last all increments are integrated into complete system. 6. Adaptive Due to the iterative nature of agile process new risks may occurs. The adaptive characteristic of agile process allows adapting the processes to attack the new risks and allows changes in the real time requirements. 7. Convergent All the risks associated with each increment are convergent in agile process by using iterative and incremental approach. 8. Collaborative As agile process is modular in nature, it needs a good communication among software development team.Different modules need to be integrated at the end of the software development process. 9. People Oriented In the agile processes customer satisfaction is the first priority over the technology and process. A good software development team increases the performance and productivity

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of the software. ADVANTAGES 1) Adaptive to the changing environment: In agile software development method, software is developed over several iterations. Each iteration is characterized by analysis, design, implementation and testing. After each iteration the mini project is delivered to the customer for their use and feedback. Any changes that upgrade the software are welcome from the customer at any stage of development and that changes are implemented. 2) Ensures customer satisfaction: This methodology requires active customer involvement throughout thedevelopment. The deliverables developed after each iteration is given to the user for use and improvement is done based on the customer feedback only. So at the end what we get as the final product is of high quality and it ensures the customer satisfaction as the entire software is developed based on the requirements taken from customer. 3) Least documentation: The documentation in agile methodology is short and to the point though it depends on the agile team. Generally they don’t make documentation on internal design of the software. The main things which should be on the documentation are product features list, duration for each iteration and date. This brief documentation saves time of development and deliver the project in least possible time. 4) Reduces risks of development: As the incremented mini software is delivered to the customers after every short development cycle and feedbacks are taken from the customers, it warns developers about the upcoming problems which may occur at the later stages of development. It also helps to discover errors quickly and they are fixed immediately. DISADVANTAGES

1) Customer interaction is the key factor of developing successful software: Agile methodology is based on customer involvement because the entire project is developed according to the requirements given by the customers. So if the customer representative is not clear about the product features, the development process will go out of the tr ack. 2) Lack of documentation: Though the least documentation saves development time as an advantage of agile method, on the other hand it is a big disadvantage for developer. Here the internal design is getting changed again and again depending on user requirements after every iteration, so it is not possible to maintain the detail documentation of design and implementation because of project deadline. So because of less available information,

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it is very difficult for the new developers who join the development team at the later stage to understand the actual method followed to develop the software. 3) Time consuming and wastage of resources because of constant change of requirements: If the customers are not satisfied by the partial software developed by certain iteration and they change their requirements then that incremented part is of no use. So it is the total wastage of time, effort and resources required to develop that increment. 4) More helpful for management than developer: The agile methodology helps management to take decisions about the software development, set goals for developers and fix the deadline for them. But it is very difficult for the baseline developers to cope up with the ever changing environment and every time changing the design, code based on just in time requirements.

COMPARISON OF AGILE PROCESS WITH OTHER SDLC MODELS TABLE I. Features Definition

Adaptability Testing Phase

PRCOESS MODELS Different Process Models Agile Process Spiral Model RAD Model Agile process is Spiral model is RAD model is the ability to both the “high speed create and software adaptation of respond development linear sequential tochanging model which model, in which requirements of focuses on component based software. managing risks. construction is used.

y y Unit, Integration , Unit, Integration System testing and System testing Quality Factors y y Risk Analysis n y Off-the- Tools n n Failure normally Code Code

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due to Knowledge Required Entry & exit Criteria Mock up Extendability Project management involvement Higher Reliability Time Boxing

design Product domain n

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1.7Choosing the right process

Software process consists of four fundamental activities: 1.Software specification where engineers or/and customers define what the product should do and how should it operate. 2. Software development is designing and actual coding. 3.Software validation is generally testing. It is important to check if the system is designed and implemented correctly. 4. Software evolution is modifying the system according to new needs of customer (s). Different types of software need different development process. Software process model is a simplified description of a software process that presents one view of a process. And again, choice of a view depends on the system developing, sometimes it is useful to apply a workflow model, sometimes, for example – a role/action model. Most software process models are based on one of three general models or paradigms of software development. 1. The waterfall approach. In this case the development process and all activities are divided into phases such as requirement specification, software design, implementation, testing etc. Development goes phase-by-phase. 15

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2. Iterative development. An initial system is rapidly developed from very abstract specifications. Of course, it can be reimplemented according to new, probably more detailed specifications. 3. Component-based software engineering (CBSE). The development process is done assuming some parts of the system is already exist, so the process focuses on integrating parts together rather than developing everything from scratch. Four principal dimensions to system dependability are: Availability, Reliability, Safety and Security. All of these may be decomposed into another, for example security includes integrity (ensuring that data is not damaged) and confidentiality. Reliability includes correctness, precision and timeliness. All of them are interrelated. Three complementary approaches that are used to improve the reliability of a system are: 1. Fault avoidance. Development techniques used to minimise the possibility of mistakes before they result in system faults. 2. Fault detection and removal. Identifying and solving system problems before the system is used. 3. Fault tolerance. Techniques used to ensure that some system errors doesn’t not result in failure. The process of risk analysis consists of four steps: 1. Risk identification. Potential risks that might arise are identified. These are dependent on the environment in which the system is to be used. In safety-critical systems, the principal risks are hazards that can lead to an accident. Experienced engineers, working with domain experts and professional safety advisors, should identify system risks. Group working techniques such as brainstorming may be used to identify risks. 2. Risk analysis and classification. The risks are considered separately. Those that are potentially serious and not implausible are selected for further analysis. Risks can be categorised in three ways: a. Intolerable. The system must be designed in such a way so that either the risk cannot arise or, if it does arise, it will not result in an accident. Intolerable risks are those that threaten human life or the financial stability of a business and which have a significant probability of occurrence. b. As low as reasonably practical (ALARP). The system must be designed so that the probability of an accident arising because of the hazard is minimised, subject to other considerations such as cost and delivery. ALARP risks are those which have less serious consequences or which have a low probability of occurrence.

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c. Acceptable. While the system designers should take all possible steps to reduce the probability of an ‘acceptable’ hazard arising, these should not increase costs, delivery time or other non-functional system attributes. 3. Risk decomposition. Each risk is analysed individually to discover potential root causes of that risk. Different techniques for risk decomposition exist. The one discussed in the book is Fault-tree analysis, where analyst puts hazard at the top and place different states which can lead to that hazard above. States can be linked with ‘or’ and ‘and’ symbols. Risks that require a combination of root causes are usually less probable than risks that can result from a single root cause. 4. Risk reduction assessment. Proposals for ways in which the identified risks may be reduced or eliminated are made. Three possible strategies of risk deduction that can be used are: a. Risk avoidance. Designing the system in such a way that risk or hazard cannot arise. b. Risk detection and removal. Designing the system in such a way that risks are detected and neutralised before they result in an accident. c. Damage limitation. Designing the system in such a way that the consequences of an accident are minimised. In the 1980s and 1990s, as computer control become widespread, the safety engineering community developed standards for safety critical systems specification and development. The process of safety specification and assurance is part of an overall safety life cycle that is defined in an international standard for safety management IEC 61508 (IEC, 1998). Security and safety requirements have something in common; however, there are some differences between these types of requirements. .

UNIT II REQUIREMENT MANAGEMENT 17

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2.1 Functional requirements and Quality attributes Quality attributes, such as response time, accuracy,security, reliability, are properties that affect the systemas a whole. Most approaches deal with quality attributes separately from the functional requirements of a system.This means that the integration is difficult to achieve and usually is accomplished only at the later stages of thesoftware development process. Furthermore, current approaches fail in dealing with the crosscutting nature ofsome of those attributes, i.e. it is difficult to represent clearly how these attributes can affect severalrequirements simultaneously. Since this integration is not supported from requirements to the implementation,some of the software engineering principles, such as abstraction, localization, modularisation, uniformity andreusability, can be compromised. What we propose is a model to identify and specify quality attributes that crosscut requirements including their systematic integration into the functional description at an early stage of the software development process, i.e. at requirements. A model for early quality attributes The process model we propose is UML compliant and is composed of three main activities: identification, specification and integration of requirements. The first activity consists of identifying all the requirements of asystem and select from those the quality attributes relevant to the application domain and stakeholders. Thesecond activity is divided into two main parts: (1)specifying functional requirements using a use case based approach; (2) describe quality attributes using special templates and identify those that cut across (i.e. crosscutting) functional requirements. The third activity proposes a set of models to represent the integration of crosscutting quality attributes and functional requirements. Figure 1 depicts this model.

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To identify the crosscutting nature of some of the quality attributes we need to take into account the informationcontained in rows Where and Requirements. If a quality attribute cuts across (i.e. is required by) several requirements and models, then it is crosscutting. The integration is accomplished by “weaving” the quality attributes with the functional requirements in three different ways : (1) Overlap: the quality attribute adds new behaviour to the functional requirements it transverses. In this case, the quality attribute may be required before those requirements, or, it may be required after them. (2) Override: the quality attribute superposes the functional requirements it transverses. In this case, its behaviour substitutes the functional requirements behavior. (3) Wrap: the quality attribute “encapsulates” the requirements it transverses. In this case the behaviour of the requirements is wrapped by the behaviour of the quality attribute. We weave quality attributes with functionalrequirements by using both standard diagrammatic representations (e.g. use case diagram, interaction diagrams) and by new diagrams. Identify requirements 19

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Requirements of a system can be classified into functional and non-functional (i.e. quality attributes). Functional requirements are statements of services the system should provide, how the system should react to particular inputs and how the system should behave in particular situations. Different types of methods are used to specify functional requirements. Use case driven approaches describe “the ways in which a user uses a system” that is why use case diagram is often used for capturing functional requirements. Quality attributes define global properties of a system. Usually these are only dealt with in the later stages of a software development process, such as design andimplementation. Identify actors and use cases. For the road pricing system, the actors we identified are: Vehicle owner: is responsible for registering a vehicle; Vehicle driver: comprehends the vehicle, the driver and the gizmo installed on it; Bank: represents the entity that holds the vehicle owner’s account; System clock: represents the internal clock of the system that monthly triggers the calculation of debits. The following are the use cases required by the actorslisted above: Register vehicle: is responsible for registering a vehicle and its owner, and communicate with the bank to guarantee a good account; Pass single toll: is responsible for dealing with tolls where vehicles pay a fixed amount. It reads thevehicle gizmo and checks on whether it is a good one. If the gizmo is ok the light is turned green, andthe amount to be paid is calculated and displayed. If the gizmo is not ok, the light is turned yellow and aphoto is taken. Enter motorway: checks the gizmo, turns on the light and registers an entrance. If the gizmo is invalid a photo is taken and registered in the system. Exit motorway: checks the gizmo and if the vehicle has an entrance, turns on the light accordingly, calculates the amount to be paid (as a function of the distance travelled), displays it and records this passage. If the gizmo is not ok, or if the vehicle did not enter in a green lane, the light is turned yellow and a photo is taken. Pay bill: sums up all passages for each vehicle, issues a debit to be sent to the bank and a copy to the vehicle owner. Identify quality attributes. Quality attributes can be assumptions, constraints or goals of stakeholders. By analysing the initial of set requirements, the potential quality attributes are identified. For example, if the owner of a vehicle has to indicate, during registration, his/her bank details so that automatic transfers can be performed automatically, then security is an issue that the system needs to address. Another fundamental quality attribute is response time that is a issue when a vehicle passes a toll gate, or when a customer activates his/her own gizmo in an ATM: the toll gate    

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components have to react in time so that the driver can see the light and the amount being displayed. Other concerns are identified in a similar fashion: Multiuser System, Compatibility, Legal Issues, Correctness and Availability. 3.2 Specify functional requirements and quality attributes The functional requirements are specified using the UML models, such as use cases, sequence and class diagrams. The quality attributes are described in templates of the form presented in Figure 2. Build the use case diagram. The set of all use cases can be represented in a use case diagram, where we can see the existing relationshipsbetween use cases and the ones between use cases and actors. Figure 3 shows the use case diagram of the roadtraffic system.

Integrate functional requirements withcrosscutting quality attributes Integration composes the quality attributes with the functional requirements, to obtain the whole system. We use UML diagrams to show the integration. The two examples given above (for response time and security) fall into two of the categories already described: overlap and wrapper. We could extend the UML diagrams to represent some quality attributes. For example, the sequence diagram shown in Figure 4 can be extended to show how response time affects a scenario

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2.2 Elicitation techniques

A major goal of Requirements Elicitation is to avoid the confusions between stakeholders and analysts. This will often involve putting significant sort into requirements elicitation. Unfortunately, Requirements Engineering is an immature discipline, perhaps not entirely unfairly characterized as a battlefield occupied by competing commercial methods, firing competing claims at each other, and leaving the consumers weary and confused. The goal of this paper is to analyze and compare of the different methods of the requirements elicitation process, which will be useful to compare the different characteristics and the performance of the different elicitation methods. Hence, all the requirement elicitation techniques are very handy for extracting the requirements and different organizations, which can use different requirement elicitation techniques according to organizational culture and needs. As requirements elicitation is a process in which intensive interaction between stakeholders and the analysts, so for finding the interaction between stakeholders and analysts will be easy for improving the quality of extracted requirements. It is important to distinguish different elicitation methods according to the four methods of communication . 1. Conversational 2. Observational 3. Analytic 4. Synthetic Each category presents a specific interaction model between analysts and stakeholders. Understanding the method category helps engineers understand different elicitation methods and guides them to select appropriate method for requirements elicitation. Four Methods of Communication i. Conversational Methods

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The conversational method provides a means of verbal communication between stakeholders and Analysts. As conversation is a natural way of communication and an effective mean of expressing needs and ideas, and the conversational methods are used massively to understand the problems and to elicit generic product requirements. The Conversational Methods are also known as verbal methods, such as Interviews, Questionnaire, and Brainstorming. a.Interviews: A typical conversational method is interviews. It is most commonly used method in requirements elicitation. An Interview is generally conducted by an experienced analyst, who has some generic knowledge about the application domain as well. In an interview, Analyst discusses the desired product with different stakeholders and develops an understanding of their requirements. Generally Interviews are divided in two groups. 1. Closed Interview: In this interview the requirements, we have to prepare some predefined questions and try to get the answers for these questions for the stakeholder. 2. Open-ended Interview: In this interview, we do not need to prepare any predefined questions, and the information from the stakeholders in open discussions. b.Questionnaire: Questionnaires are one of the methods of gathering requirements in less cost. Questionnaires reach a large number of people, not only in less time but also in a lesser cost. The general factors which affect the usage of the questionnaire are 1. The available resources to gather the requirements mainly depends on the available resource 2. Type of Requirements that has to be gathering depends on the level of the respondent’s knowledge and background. 3 Anonymity provided to the respondent c.Brainstorming : Brainstorming is another conversation method. It has some similarities with workshops and focus groups as in Brainstorming stakeholders are gather together for a short time period but in this short time period they develop a large and broad list of ideas. In this meeting “out -of-the-box” thinking approach is encouraged. The brainstorming involves both idea generation and idea reduction. Conversation is one of the most prevalent yet invisible forms of social interaction. People are usually happy to describe their work and difficulties they face. The verbally expressive demands, needs and constraints are often called non-tacit requirements. Conversational methods are very commonly used in requirements development. However, they are labor intensive : meeting setup and transcript producing and analyzing from records of a live interaction take time. iiObservational Methods :

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The observational method provides means to develop a better understanding about domain of Application. Observation methods work by observing human activities at environment where system is expected to be deployed. In addition to state able requirements, some requirements are apparent to stakeholders, but stakeholders find it very hard to verbalize. The observation methods come into play where Verbal communication becomes helpless for collecting tacit requirements. Therefore, observing how people carry out their routine work forms a means of acquisition of information which are hard to verbalize. The observational methods appear to be well suited when stakeholders find it difficult to state their needs and when analysts are looking for a better understanding of the context in which the desired product is expected to be used. Observational methods is including, Social analysis, Observation, Ethnographic study, and protocol analysis. Social analysis, Observation, Ethnographic study: An observer spends some time in a society or culture for making detailed observation of all their practices. This practice gives the initial understanding of system, work flow and organizational culture. Protocol analysis: In protocol analysis a stakeholder is observed when he is engaged in some task, and concurrently speaks out loud and explains his thought. With the protocol analysis it is easy to identify Interaction problems in existing systems and it gives better and closer understanding of Work context and work flow.

For Observational methods, the observer must be accepted by the people being studied and the people being studied should carry on with their normal activities as if the observer is not there. In both Conversational and Observation methods, requirement elicitation is done by studying some individuals but a variety of documentation may prove out to be handy for extracting the requirements of the desired product. The documentation may include problem analysis, organizational charts, standards, user manuals of existing systems, survey report of competitive systems in market, and so on. By studying these documents, engineers capture the information about the application domain, the workflow, the product features, and map it to the requirements specification. iiiAnalytic Methods : Conversational or Observational methods are used to directly extracted requirements from people’s behavior and their verbalized thought. But still there is a lot of knowledge that is not directly expressed, for example expert’s knowledge, information about regulation and legacy products are some examples of such sources. All the stated sources provide engineers 24

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rich information in relation to the product. Analytic methods provide ways to explore the existing documentation or knowledge and acquire requirements from a series of deductions.it will include Requirement reuse, documentation studies, laddering, and repertory grid Requirement reuse: In this technique, glossaries and specification of legacy systems or systems within the same product family is used to identify requirements of the desired system. It has been observed that many requirements in a new system are more or less same as they were in a legacy system’s requirement. So it is not a bad idea to reuse the details of requirements of an earlier system in a new system. Documentation studies: In this technique different available documents (e.g. Organizational policies, standards, legislation, Market information, Specification of legacy systems) are read and studied to find the content that can prove out to be relevant useful for the requirements elicitation tasks. Laddering: This technique can be divided in 3 parts: creation, reviewing and modification. Laddering method is a form of structured interview that is widely used in the field of knowledge elicitation activities to elicit stakeholder’s goals, aims and values Analyst used laddering method to create, review and modify the hierarchical contents of expert’s knowledge in the form of tree diagram. It was first introduced by the clinical psychologists in 1960 to understand the people “score values and beliefs . Its success in the fields of psychology allows other researchers in the industries to adapt it in their fields. Specifically software developers have adapted the laddering techniques for gather the complex user tacit requirements. Repertory grid: Stakeholder is asked for attributes applicable to a set of entities and values for cells in entity -attribute matrix. In general, the analytic methods are not vital to requirements elicitation, since requirements are captured indirectly from other sources, rather than end users and customers. However, they form complementary ones to improve the efficiency and effectiveness of requirements elicitation, especially when the information from legacy or related products is reusable. ivSynthetic Methods: So far, we have discussed Conversational, Observational and Analytic methods. It is apparent that No single method is sufficient enough to develop all the requirement of a system. All these methods are good and very handy in some certain context and circumstances. It is often a good idea to combine different elicitation methods for developing requirement. The combination helps the engineer uncover the basic aspects and gain a 25

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generic knowledge of the application domain. Instead of combining different of individual methods, the synthetic method forms a coherent whole by systematically combining conversation, observation, and analysis into single methods. Analysts and stakeholder representatives communicate and coordinate in different ways to reach a common understanding of the desired product. Synthetic methods are known as collaborative methods as they are collaboration of multiple requirement elicitation methods. Requirement elicitation techniques of Synthetic methods are including scenarios, passive storyboards, prototyping, interactive storyboards, JAD/RAD sessions, and Contextual inquiry . Scenarios, passive storyboards: It is an interaction session. In this session a sequence of actions and events described for executing some generic task which the system is intended to accomplish. With the help of this technique, clear requirement related to procedure and data flow can be achieved. With this technique initial set of requirement can be prepared in lesser cost. Prototyping, Interactive storyboards: In this technique, a concrete but partial system is discussed with stakeholders. This concrete but partial system is expected to be delivered at the end of project. The purpose of showing this system to stakeholders is to elicit and validate functional requirement. The p JAD/RAD session: It stands for Joint Application Development/Rapid Application Development and emphasizes user involvement through group sessions with unbiased facilitator. JAD is conducted in the same manner as brainstorming, except that the stakeholders and the users are also allowed to participate and discuss on the design of the proposed system. The discussion with the stakeholders and the users continues until the final requirements are gathered. Contextual inquiry: this technique is a combination of open-ended interview, workplace observation, and prototyping. This method used for interactive systems design where user interface design is critical. All four requirement elicitation methods are commonly used but the selection of requirement elicitation method entirely depends on the needs and organizational structure. No matter what development project is, requirements development nearly always takes place in the context of a human activity system, and problem owners are people .. It is essential for requirements engineers to study how people perceive, understand, and express the problem domain, how they interact with the desired product, and how the physical and cultural environments affect their actions.

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The conversational methods provide a direct contact channel between engineers and stakeholders, and the requirements are mainly no tacit. The observational methods provide an indirect channel by observing user’s interaction with his work setting and context, and the requirements fall into tacit knowledge. The analytic methods form one complementary indirect contact channel to extract requirements proactively. The synthetic methods focus more on collective effort on clarifying the features of desired products, and the communication channel is therefore a mix of direct contact and indirect contact. Each type of techniques has trade-offs. In reality, of course, the boundary between different types of method is blurred. Advantage and Disadvantage of Requirement Elicitation After the discussion the different of the four group of requirement elicitation method. In order to understand the each Requirement elicitation Methods and effective use them in the real case ,we have to focus on the advantages and disadvantages of different requirement elicitation methods: Conversational, Observational, Analytic and Synthetic one by one.

1) As conversation is a natural and effective way of communication, that’s why the conversational methods are used massively. Conversational methods include techniques such as: interviews, Questionnaire and Brainstorming. Advantages of Conversational Method: Conversational techniques are really helpful for collection rich information about the requirements. Along with the requirements, conversational methods uncover opinions, feelings and goals of different individuals. With the help of conversational methods it is easy to dig into the details with the help of follow up questions to what the person has told you. Disadvantages of Conversational Method: Along with the number of advantages there are certain disadvantages of conversational methods as this skill is very hard to master. Conversational Methods for requirement elicitation depend a lot on the behavior and attitude of conductor [4]. A Conductor is supposed to be neutral. As a result of conversational method, a collection of information can be obtained and getting meaningful information from gathered information will be difficult. In Conversational Methods the contexts of conversation plays a very important role as well.

2) Observational methods are helpful in understanding the application domain by observing human activities Observational methods are inefficient when the project have very tight schedule at requirement stages. Method like ethnography and protocol analysis methods falls under this category [22]. The Observational method involves: Social analysis, Observation, Ethnographic study and Protocol Analysis. 27

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Advantages of Observational Methods: The observational methods are good choice for uncovering basic aspects of routine order. Moreover they provide vital information for designing solution. Observational Methods are very handy when the development team has lack of experience about product domain. Disadvantages of Observational Methods: Along with the advantages of observational methods there are certain disadvantages as well. The Biggest disadvantage is that observation methods need a lot of time and these techniques are not good choice when schedule is tight. Just like conversational techniques, observational techniques are also hard to master [10]. Moreover observational techniques require sensitivity and responsiveness to physical environment.

3) Conversational or Observational methods are used to directly extracted requirements from people’s behavior and their verbalized thought. But still there is a lot of knowledge that is not directly expressed. For extracting this kind of knowledge and information analytical skills are used. Analytical Skills include Requirement Reuse, Documentation Studies, Laddering and Repertory Girds. Advantages of Analytical Methods: Analytic Methods have numerous advantages as “People” are not the only source of information in terms of requirements. Experts Knowledge and Opinion plays an important role in requirement maturity. Moreover, reuse of already available information saves time and cost. Analytical methods have hierarchical flow of information as well. Disadvantages of Analytical Methods: Along advantages, Analytical methods have certain disadvantages as well. The biggest disadvantage is that an analytical method requires some empirical data, documentation or expert’s opinions without these it is difficult to elicit proper requirements. Similarly analytical methods can narrow the vision of product. As analytical methods deal with some earlier knowledge so possibility of error replication is a serious and constant threat. Analytical methods are never a good choice when you are going to develop an altogether new system. [12] 2.3 Quality Attribute Workshops(QAW) The Quality Attribute Workshop (QAW) is a facilitated method that engages system stakeholders early in the life cycle to discover the driving quality attributes of a software-intensivesystem. The QAW was developed to complement the Architecture Tradeoff Analysis

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MethodSM(ATAMSM) and provides a way to identify important quality attributes and clarify systemrequirements before the software architecture has been created. This is the third edition of a technical report describing the QAW. We have narrowed the scopeof a QAW to the creation of prioritized and refined scenarios. This report describes the newlyrevised QAW and describes potential uses of the refined scenarios generated during it. The Quality Attribute Workshop (QAW) is a facilitated method that engages system stakeholdersearly in the system development life cycle to discover the driving quality attributes ofa software-intensive system. The QAW is system-centric and stakeholder focused; it is usedbefore the software architecture has been created. The QAW provides an opportunity to gatherstakeholders together to provide input about their needs and expectations with respect to keyquality attributes that are of particular concern to them

Both the system and software architectures are key to realizing quality attribute requirementsin the implementation. Although an architecture cannot guarantee that an implementation willmeet its quality attribute goals, the wrong architecture will surely spell disaster. As an example,consider security. It is difficult, maybe even impossible, to add effective security to a systemas an afterthought. Components as well as communication mechanisms and paths must bedesigned or selected early in the life cycle to satisfy security requirements. The critical qualityattributes must be well understood and articulated early in the development of a system, so thearchitect can design an architecture that will satisfy them. The QAW is one way to discover,document, and prioritize a system’s quality attributes early in its life cycle. It is important to point out that we do not aim at an absolute measure of quality; rather our purposeis to identify scenarios from the point of view of a diverse group of stakeholders (e.g.,architects, developers, users, sponsors). These scenarios can then be used by the system engineersto analyze the system’s architecture and identify concerns (e.g., inadequate 29

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performance,successful denial-of-service attacks) and possible mitigation strategies (e.g., prototyping,modeling, simulation). QAW Method The QAW is a facilitated, early intervention method used to generate, prioritize, and refinequality attribute scenarios before the software architecture is completed. The QAW is focusedon system-level concerns and specifically the role that software will play in the system. TheQAW is dependent on the participation of system stakeholders—individuals on whom the systemhas significant impact, such as end users, installers, administrators (of database managementsystems [DBMS], networks, help desks, etc.), trainers, architects, acquirers, system andsoftware engineers, and others. The group of stakeholders present during any one QAW shouldnumber at least 5 and no more than 30 for a single workshop. In preparation for the workshop,stakeholders receive a “participants handbook” providing example quality attribute taxonomies,questions, and scenarios. If time allows, the handbook should be customized to the domain of the system and contain the quality attributes, questions, and scenarios that areappropriate to the domain and the level of architectural detail available.

The contribution of each stakeholder is essential during a QAW; all participants are expectedto be fully engaged and present throughout the workshop. Participants are encouraged to commentand ask questions at any time during the workshop. However, it is important to recognizethat facilitators may occasionally have to cut discussions short in the interest of time or when itis clear that the discussion is not focused on the required QAW outcomes. The QAW is anintense and demanding activity. It is very important that all participants stay focused, are ontime, and limit side discussions throughout the day. The QAW involves the following steps: 1. QAW Presentation and Introductions 2. Business/Mission Presentation 3. Architectural Plan Presentation 4. Identification of Architectural Drivers 5. Scenario Brainstorming 6. Scenario Consolidation 7. Scenario Prioritization 8. Scenario Refinement The following sections describe each step of the QAW in detail. Step 1: QAW Presentation and Introductions

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In this step, QAW facilitators describe the motivation for the QAW and explain each step ofthe method. We recommend using a standard slide presentation that can be customizeddepending on the needs of the sponsor. Next, the facilitators introduce themselves and the stakeholders do likewise, briefly statingtheir background, their role in the organization, and their relationship to the system being built. Step 2: Business/Mission Presentation After Step 1, a representative of the stakeholder community presents the business and/or missiondrivers for the system. The term “business and/or mission drivers” is used carefully here.Some organizations are clearly motivated by business concerns such as profitability, whileothers, such as governmental organizations, are motivated by mission concerns and find profitabilitymeaningless. The stakeholder representing the business and/or mission concerns (typicallya manager or management representative) spends about one hour presenting • the system’s business/mission context • high-level functional requirements, constraints, and quality attribute requirements During the presentation, the facilitators listen carefully and capture any relevant informationthat may shed light on the quality attribute drivers. The quality attributes that will be refined inlater steps will be derived largely from the business/mission needs presented in this step. Step 3: Architectural Plan Presentation While a detailed system architecture might not exist, it is possible that high-level systemdescriptions, context drawings, or other artifacts have been created that describe some of thesystem’s technical details. At this point in the workshop, a technical stakeholder will presentthe system architectural plans as they stand with respect to these early documents. Informationin this presentation may include • plans and strategies for how key business/mission requirements will be satisfied • key technical requirements and constraints—such as mandated operating systems, hardware, middleware, and standards—that will drive architectural decisions • presentation of existing context diagrams, high-level system diagrams, and other writtendescriptions Step 4: Identification of Architectural Drivers During steps 2 and 3, the facilitators capture information regarding architectural drivers thatare key to realizing quality attribute goals in the system. These drivers often include high-levelrequirements, business/mission concerns, goals and objectives, and various quality 31

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attributes.Before undertaking this step, the facilitators should excuse the group for a 15minute break,during which they will caucus to compare and consolidate notes taken during steps 2 and 3. When the stakeholders reconvene, the facilitators will share their list of key architectural driversand ask the stakeholders for clarifications, additions, deletions, and corrections. The idea isto reach a consensus on a distilled list of architectural drivers that include high-level requirements,business drivers, constraints, and quality attributes. The final list of architectural driverswill help focus the stakeholders during scenario brainstorming to ensure that theseconcerns are represented by the scenarios collected. Step 5: Scenario Brainstorming After the architectural drivers have been identified, the facilitators initiate the brainstormingprocess in which stakeholders generate scenarios. The facilitators review the parts of a goodscenario (stimulus, environment, and response) and ensure that each scenario is well formedduring the workshop.

Each stakeholder expresses a scenario representing his or her concerns with respect to the systemin round-robin fashion. During a nominal QAW, at least two round-robin passes are madeso that each stakeholder can contribute at least two scenarios. The facilitators ensure that atleast one representative scenario exists for each architectural driver listed in Step 4.Scenario generation is a key step in the QAW method and must be carried out with care. Wesuggest the following guidance to help QAW facilitators during this step: 1. Facilitators should help stakeholders create well-formed scenarios. It is tempting forstakeholders to recite requirements such as “The system shall produce reports for users.”While this is an important requirement, facilitators need to ensure that the quality attributeaspects of this requirement are explored further. For example, the following scenario shedsmore light on the performance aspect of this requirement: “ A remote user requests a databasereport via the Web during peak usage and receives the report within five seconds.”Note that the initial requirement hasn’t been lost, but the scenario further explores the performanceaspect of this requirement. Facilitators should note that quality attribute names by themselves are not enough. Rather than say “the system shall be modifiable,” the scenarioshould describe what it means to be modifiable by providing a specific example of amodification to the system vis-à-vis a scenario. 2. The vocabulary used to describe quality attributes varies widely. Heated debates oftenrevolve around to which quality attribute a particular system property belongs. It

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doesn’tmatter what we call a particular quality attribute, as long as there’s a scenario thatdescribes what it means. 3. Facilitators need to remember that there are three general types of scenarios and to ensurethat each type is covered during the QAW: a. use case scenarios - involving anticipated uses of the system b. growth scenarios - involving anticipated changes to the system c. exploratory scenarios - involving unanticipated stresses to the system that can includeuses and/or changes 4. Facilitators should refer to the list of architectural drivers generated in Step 4 from time totime during scenario brainstorming to ensure that representative scenarios exist for eachone. Step 6: Scenario Consolidation After the scenario brainstorming, similar scenarios are consolidated when reasonable.To do that, facilitators ask stakeholders to identify those scenarios that are very similar in content.Scenarios that are similar are merged, as long as the people who proposed them agree andfeels that their scenarios will not be diluted in the process. Consolidation is an important stepbecause it helps to prevent a “dilution” of votes during the prioritization of scenarios (Step 7).Such a dilution occurs when stakeholders split their votes between two very similar scenarios.As a result, neither scenario rises to importance and is therefore never refined (Step 8). However,if the two scenarios are similar enough to be merged into one, the votes might be concentrated,and the merged scenario may then rise to the appropriate level of importance and berefined further.Facilitators should make every attempt to reach a majority consensus with the stakeholdersbefore merging scenarios. Though stakeholders may be tempted to merge scenarios with abandon,they should not do so. In actuality, very few scenarios are merged. Step 7: Scenario Prioritization Prioritization of the scenarios is accomplished by allocating each stakeholder a number ofvotes equal to 30% of the total number of scenarios generated after consolidation. The actualnumber of votes allocated to stakeholders is rounded to an even number of votes at the discretionof the facilitators. For example, if 30 scenarios were generated, each stakeholder gets 30 x0.3, or 9, votes rounded up to 10. Voting is done in round-robin fashion, in two passes. . Stakeholders can allocate any number oftheir votes to any scenario or combination of scenarios. The votes are counted, and the scenariosare prioritized accordingly. Step 8: Scenario Refinement

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After the prioritization, depending on the amount of time remaining, the top four or five scenariosare refined in more detail. Facilitators further elaborate each one, documenting the following: • Further clarify the scenario by clearly describing the following six things: 1. stimulus - the condition that affects the system 2. response - the activity that results from the stimulus 3. source of stimulus - the entity that generated the stimulus 4. environment - the condition under which the stimulus occurred 5. artifact stimulated - the artifact that was stimulated 6. response measure - the measure by which the system’s response will be evaluated • Describe the business/mission goals that are affected by the scenario. • Describe the relevant quality attributes associated with the scenario. • Allow the stakeholders to pose questions and raise any issues regarding the scenario. Suchquestions should concentrate on the quality attribute aspects of the scenario and any concernsthat the stakeholders might have in achieving the response called for in the scenario.See the example template for scenario refinement in Appendix A. This step continues untiltime runs out or the highest priority scenarios have been refined. Typically, time runs out first. QAW Benefits The QAW provides a forum for a wide variety of stakeholders to gather in one room at onetime very early in the development process. It is often the first time such a meeting takes placeand generally leads to the identification of conflicting assumptions about system requirements.In addition to clarifying quality attribute requirements, the QAW provides increased stakeholdercommunication, an informed basis for architectural decisions, improved architecturaldocumentation, and support for analysis and testing throughout the life of the system.

The results of a QAW include • a list of architectural drivers • the raw scenarios • the prioritized list of raw scenarios • the refined scenarios This information can be used to • update the organization’s architectural vision • refine system and software requirements • guide the development of prototypes • exercise simulations 34

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• understand and clarify the system’s architectural drivers • influence the order in which the architecture is developed • describe the operation of a system In short, the architect can use this information to design the architecture. In addition, after thearchitecture is created, the scenarios can be used as part of a software architecture evaluation.If the Architecture Tradeoff Analysis MethodSM (ATAMSM)4 is selected as the software architectureevaluation method, the scenarios generated during the QAW can be incorporated asseed scenarios in that evaluation . The QAW lends itself well to the capture of many architecturally relevant materials. Softwarearchitectural documentation is a collection of view packets plus any documentation thatapplies to more than one view [Clements 02b]. Each view packet contains a primary presentation, a catalog of the view’s elements (including element behavior), a context diagram, a variabilityguide, architecture background (rationale, analysis results, and assumptions about theenvironment), and other information including mapping to requirements. Several pieces of this information will be gleaned directly from the QAW. For example, scenariogeneration can lead to the creation of use case diagrams, context diagrams, or theirequivalent. Refined scenarios can be documented as sequence diagrams or collaboration diagrams.Stakeholders’ concerns and any other rationale information that is captured should berecorded individually in a form that can be included in the appropriate view packet or overviewdocumentation. Details that explain how to transition these artifacts into architecturaldocumentation is the subject of ongoing research.In addition to the more immediate benefits cited above, the scenarios continue to provide benefitsduring later phases of development. They provide input for analysis throughout the life ofthe system and can be used to drive test case development during implementation testing. 2.4 Analysis ,prioritization,and Method(ACDM)

trade

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Just as blueprints in the building construction industry guides the construction of a building, the software architecture serves a blueprint that addresses technical concerns and programmatic issues of a project. An architectural focus will: help refine the functional requirements, quality attribute requirements, and constraints help set and maintain expectations in stakeholders define the team structure aid in creating more accurate project estimates    

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  

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establish the team vocabulary help identify technical risk early guide the creation of a more realistic and accurate production schedule and assist in project tracking and oversight provide an early vision of the solution/system

A number of methods have been created by the Software Engineering Institute to help practitioners create better architectures. Some of these methods include: Quality Attribute Workshop (QAW) ,Architecture Tradeoff Analysis Method (ATAM) ], Attribute Driven Design (ADD). These methods have provided great value to practitioners trying to build better architectures. However, these methods have two main problems. First, they are intervention oriented. These methods were not designed with a particular development philosophy (lifecycle or process) in mind. As such, they do not fit neatly into existing development models or processes without significant tailoring Little guidance exists that describes how to tailor these methods to fit into an organization’s development model. To maximize their effectiveness, these methods should be used together and this requires significant tailoring. In order to tailor these methods, someone in an organization has to know a great deal about each of them in order to tease them apart, and reassemble them into a cohesive, usable development method/process. This is a risky and difficult proposition in many organizations. The second problem with these methods is that in their originally authored form they tend to be heavy-weight and expensive for the smaller teams, projects, short deadlines, and iterative deliveries. Overcoming these two hurdles has prevented many organizations in industry from embracing these methods, and more importantly, adopting the entire body of work. Organizations are constantly bombarded with emerging methods, tools, and techniques and they must: • figure out if they are useful • how to use them • how to make them fit together • estimate the costs for adoption • show return on investment After 20 years of process model promises, this is a tough sell in most organizations. Just as technological components can have mismatch, so can processes, methods, and tools when we try to bring them together in an organization. Software development teams need specific guidance about how to create software architecture in the context of a product development

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lifecycle. ACDM brings together some of the best practices into a lifecycle development model. The key goals of ACDM are to help software development teams: 

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Get the information from stakeholders needed to define the architecture as early as possible. Create, refine, and update the architecture in an iterative way throughout the lifecycle whether the lifecycle is waterfall or iterative. Validate that the architecture will meet the expectations once implemented. Define meaningful roles for team members to guide their efforts. Create better estimates and schedules based on the architectural blueprint. Provide insight into project performance. Establish a lightweight, scalable, tailorable, repeatable process framework.

The ACDM is geared toward organizations and teams building software intensive systems and puts the software architecture “front-and-center” during all phases of the project. The method prescribes creating a notional architecture as soon as the most preliminary requirements work has been completed. The architecture is developed early and iteratively refined as a central focus of the project. The architecture is refined until the development team is confident that a system can be implemented and it will meet the needs of the stakeholder community. In ACDM, the architecture is the locus for defining all subsequent processes, planning, activities, and artifacts. Preconditions for beginning ACDM are defining roles for all of the team members. The method describes several roles and their responsibilities. The ACDM essentially follows seven prescribed stages briefly described below.

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While ACDM emerged from small teams and projects (4 to 6 team members, 1 to 2 year projects), it is designed to scale up to meet the needs of larger teams and projects as well. In larger projects, the ACDM is used by a core architecture team to create and refine the overall system architecture. The output from this ACDM cycle is an initial partitioning of the system (or system of systems) into sub-elements (or subsystems) and their interactions. Detailed architecting of the various elements is deferred to smaller teams, each using ACDM to architect their part of the system (which may be another system). Later integration of the entire system is undertaken in production stages 6 and 7. The ACDM has been evolved over a five year period (since 1999) on small projects and is now being further refined for use on larger projects in industry. ACDM Preconditions A precondition to beginning step 1 of ACDM is to establish the team roles for project. The recommended roles and responsibilities for ACDM are listed in the table below:

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The ACDM also assumes that the functional requirements and constraints exist but does not discuss in detail how to get them, document them, and organize them. This may seem somewhat naive but this is intentional since requirement gathering, documenting, and organization varies widely even in our small studio projects. While ACDM does not address the gathering of initial requirements and constraints, it will help refine them, clarify them, as the architecture is designed and matures. The relative completeness of the functional requirements varies from project to project and may have to be discovered and refined as a consequence of building the system. Some clients provide a documented list of functional requirements; others just bring ideas to the team. The initial gathering of functional requirements is assumed to have occurred prior to beginning step 1 of ACDM. The requirements engineer will coordinate the gathering and documenting of functional requirements. The term “constraints” as applied in this context can be confusing. A “constraint” is an imposed design decision or a design decision that the architect is not at liberty to make or change. Example constraints include being forced to use a particular operating system, use a particular commercial off-the-shelf product, adhere to a particular standard, or build a system using a prescribed implementation framework. 2.5 Requirements documentation and specification A Software requirements specification (SRS), a requirements specification for a software system, is a description of the behavior of a system to be developed and may include a set of use cases that describe interactions the users will have with the software. In addition it also contains non-functional requirements. Non-functional requirements impose constraints on the design or implementation (such as performance engineering requirements, quality standards, or design constraints) .

Software requirements specification establishes the basis for agreement between customers and contractors or suppliers (in market-driven projects, these roles may be played by the 39

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marketing and development divisions) on what the software product is to do as well as what it is not expected to do. Software requirements specification permits a rigorous assessment of requirements before design can begin and reduces later redesign. It should also provide a realistic basis for estimating product costs, risks, and schedules. The software requirements specification document enlists enough and necessary requirements that are required for the project development.To derive the requirements we need to have clear and thorough understanding of the products to be developed or being developed. This is achieved and refined with detailed and continuous communications with the project team and customer till the completion of the software. 2.6 Change management Globalization and the constant innovation of technology result in a constantly evolving business environment. Phenomena such as social media and mobile adaptability have revolutionized business and the effect of this is an ever increasing need for change, and therefore change management. The growth in technology also has a secondary effect of increasing the availability and therefore accountability of knowledge. Easily accessible information has resulted in unprecedented scrutiny from stockholders and the media and pressure on management.

With the business environment experiencing so much change, organizations must then learn to become comfortable with change as well. Therefore, the ability to manage and adapt to organizational change is an essential ability required in the workplace today. Yet, major and rapid organizational change is profoundly difficult because the structure, culture, and routines of organizations often reflect a persistent and difficult-to-remove "imprint" of past periods, which are resistant to radical change even as the current environment of the organization changes rapidly.[10] Due to the growth of technology, modern organizational change is largely motivated by exterior innovations rather than internal moves. When these developments occur, the organizations that adapt quickest create a competitive advantage for themselves, while the companies that refuse to change get left behind. This can result in drastic profit and/or market share losses. Organizational change directly affects all departments from the entry level employee to senior management. The entire company must learn how to handle changes to the organization.

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Choosing what changes to implement When determining which of the latest techniques or innovations to adopt, there are four major factors to be considered: 1. Levels, goals, and strategies 2. Measurement system 3. Sequence of steps 4. Implementation and organizational change Managing the change process Regardless of the many types of organizational change, the critical aspect is a company’s ability to win the buy-in of their organization’s employees on the change. Effectively managing organizational change is a four-step process: 1. Recognizing the changes in the broader business environment 2. Developing the necessary adjustments for their company’s needs 3. Training their employees on the appropriate changes 4. Winning the support of the employees with the persuasiveness of the appropriate adjustments

As a multi-disciplinary practice that has evolved as a result of scholarly research, organizational change management should begin with a systematic diagnosis of the current situation in order to determine both the need for change and the capability to change. The objectives, content, and process of change should all be specified as part of a Change Management plan. Change management processes should include creative marketing to enable communication between changing audiences, as well as deep social understanding about leadership’s styles and group dynamics. As a visible track on transformation projects, Organizational Change Management aligns groups’ expectations, communicates, integrates teams and manages people training. It makes use of performance metrics, such as financial results, operational efficiency, leadership commitment, communication effectiveness, and the perceived need for change to design appropriate strategies, in order to avoid change failures or resolve troubled change projects. Successful change management is more likely to occur if the following are included: 1. Benefits management and realization to define measurable stakeholder aims, create a business case for their achievement (which should be continuously updated), and monitor assumptions, risks, dependencies, costs, return on investment, dis-benefits and cultural issues affecting the progress of the associated work

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2. Effective communication that informs various stakeholders of the reasons for the change (why?), the benefits of successful implementation (what is in it for us, and you) as well as the details of the change (when? where? who is involved? how much will it cost? etc.) 3. Devise an effective education, training and/or skills upgrading scheme for the organization 4. Counter resistance from the employees of companies and align them to overall strategic direction of the organization 5. Provide personal counseling (if required) to alleviate any change-related fears 6. Monitoring of the implementation and fine-tuning as required Examples Mission changes Strategic changes Operational changes (including Structural changes) Technological changes Changing the attitudes and behaviors of personnel Personality Wide Changes 

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2.7 Traceability of requirements Traceability is the ability to verify the history, location, or application of an item by means of documented recorded identification.

Other common definitions include to capability (and implementation) of keeping track of a given set or type of information to a given degree, or the ability to chronologically interrelate uniquely identifiable entities in a way that is verifiable. Measurement The term "measurement traceability" is used to refer to an unbroken chain of comparisons relating an instrument's measurements to a known standard. Calibration to a traceable standard can be used to determine an instrument's bias, precision, and accuracy. It may also be used to show a chain of custody - from current interpretation of evidence to the actual evidence in a legal context, or history of handling of any information. In many countries, national standards for weights and measures are maintained by a National Measurement Institute (NMI) which provides the highest level of standards for thecalibration / measurement traceability infrastructure in that country. Examples of government agencies include the National Physical Laboratory, UK (NPL) the National Institute of Standards and Technology (NIST) in the USA, the PhysikalischTechnischeBundesanstalt (PTB) in Germany, and the IstitutoNazionale di RicercaMetrologica (INRiM) in Italy. As defined by NIST, "Traceability of measurement

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requires the establishment of an unbroken chain of comparisons to stated references each with a stated uncertainty." Logistics In logistics, traceability refers to the capability for tracing goods along the distribution chain on a batch number or series number basis. Traceability is an important aspect for example in the automotive industry, where it makes recalls possible, or in the food industry where it contributes to food safety. The international standards organization EPCglobal under GS1 has ratified the EPCglobal Network standards (especially the EPC Information Services EPCIS standard) which codify the syntax and semantics for supply chain events and the secure method for selectively sharing supply chain events with trading partners. These standards for traceability have been used in successful deployments in many industries and there are now a wide range of products that are certified as being compatible with these standards. Materials In materials, traceability refers to the capability to associate a finished part with destructive test results performed on material from the same ingot with the same heat treatment, or to associate a finished part with results of a test performed on a sample from the same melt identified by the unique lot number of the material. Destructive tests typically include chemical composition and mechanical strength tests. A heat number is usually marked on the part or raw material which identifies the ingot it came from, and a lot number may identify the group of parts that experienced the same heat treatment (i.e., were in the same oven at the same time). Material traceability is important to the aerospace, nuclear, and process industry because they frequently make use of high strength materials that look identical to commercial low strength versions. In these industries, a part made of the wrong material is called "counterfeit," even if the substitution was accidental. Supply chain In the supply chain, traceability is more of an ethical or environmental issue. Environmentally friendly retailers may choose to make information regarding their supply chain freely available to customers, illustrating the fact that the products they sell are manufactured in factories with safe working conditions, by workers that earn a fair wage, using methods that do not damage the environment. Software development In software development, the term traceability (or Requirements Traceability) refers to the ability to link product requirements back to stakeholders' rationales and forward to corresponding design artifacts, code, and test cases. Traceability supports numerous software engineering activities such as change impact analysis, compliance verification or traceback of code, regression test selection, and requirements validation. It is usually accomplished in the form of a matrix created for the verification and validation of the project. Unfortunately 43

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the practice of constructing and maintaining a requirements trace matrix (RTM) can be very arduous and over time the traces tend to erode into an inaccurate state unless date/time stamped. Alternate automated approaches for generating traces using information retrieval methods have been developed. In transaction processing software, traceability implies use of a unique piece of data (e.g., order date/time or a serialized sequence number) which can be traced through the entire software flow of all relevant application programs. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability key to find the particular transaction. This is also sometimes referred to as the transaction footprint. Food processing In food processing (meat processing, fresh produce processing), the term traceability refers to the recording through means of barcodes or RFID tags & other tracking media, all movement of product and steps within the production process. One of the key reasons this is such a critical point is in instances where an issue of contamination arises, and a recall is required. Where traceability has been closely adhered to, it is possible to identify, by precise date/time & exact location which goods must be recalled, and which are safe, potentially saving millions of dollars in the recall process. Traceability within the food processing industry is also utilised to identify key high production & quality areas of a business, versus those of low return, and where points in the production process may be improved. In food processing software, traceability systems imply the use of a unique piece of data (e.g., order date/time or a serialized sequence number, generally through the use of abarcode / RFID) which can be traced through the entire production flow, linking all sections of the business, including suppliers & future sales through the supply chain. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability software to find the particular transaction and/or product within the supply chain. Forest products Within the context of supporting legal and sustainable forest supply chains, traceability has emerged in the last decade as a new tool to verify claims and assure buyers about the source of their materials. Mostly led out of Europe, and targeting countries where illegal logging has been a key problem (FLEGT countries), timber tracking is now part of daily business for many enterprises and jurisdictions. Full traceability offers advantages for multiple partners along the supply chain beyond certification systems, including: Mechanism to comply with local and international policies and regulations. Reducing the risk of illegal or non-compliant material entering the supply chains. Providing coordination between authorities and relevant bodies. 

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Allowing automatic reconciliation of batches and volumes available. Offering a method of stock control and monitoring. Triggering real-time alerts of non-compliance. Reducing likelihood of recording errors. Improving effectiveness and efficiency. Increasing transparency. Promoting company integrity.

UNIT III ESTIMATION,PLANNING AND TRACKING 3.1 Identifying and Prioritizing Risks

The formal process by which risks factors are systematically identified, assessed, and responded to. Risk management concentrates on identifying and controlling areas or events that have a potential of causing unwanted change. (Note that opportunities, also known as positive risk, should also be managed/exploited. This document is focused on mitigating negative risk, rather than maximizing positive risk.) Definitions, Acronyms, and Abbreviations

A potential undesirable and unplanned event or circumstance , anticipated in advance, which could prevent the project from meeting one or more of its objectives. Issue An event or circumstance that has occurred with project impact that needs to be managed and resolved, with escalation if appropriate. Task / Work packages from the Work Breakdown Structure (WBS) Action Item or work resulting from project meetings or conversations. Risk

Risk Management Approach

The project team will implement a continuous risk management process which entails two major processes – risk assessment and risk mitigation. Risk assessment includes activities to identify risks, analyze and prioritize. Risk mitigation includes developing risk contingency and mitigation strategies, as well as monitoring the 45

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impact of the issue, action items, strategies and residual risks.

Risk

Risk Analysis and

Risk Response

Risk Monitoring

Identification

Prioritization

Planning

and Control

Risk Tolerance Communication The company has a very low threshold for risks to: o The client experience o The experience of users who directly support the client o Non-public information (NPI) o Potential for fraud or loss related to insufficient control or security Risk Management Tasks Risk Management activities are documented in the Risk Management workbook. workbook is used to identify, prioritize, analyze, and plan a risk response.

The

Risk Identification: The process of determining which risks may affect the project and documenting their characteristics. Risk Assessment:The Risk Assessement and Mitigation tab in the Risk Management workbook has a set of questions that need to be answered that help determine the risk level of the project. Each question has a potential rating of High, Medium, or Low in terms of potential impact. Risk Register: This is located on the project’s SharePoint site where project specific risks can be entered. All risks identified through any means should be entered individually in the Risk Register on SharePoint. Like all company documentation, discretion should be used in documenting risk: all statements should be fact-based and conclusions should be reviewed by management (and if appropriate, Legal.) Risks should be stated in a standard format, to help the team stay focused on risks versus root causes and results: Cause – Risk – Effect. o Cause: specific situation that introduces risk o Risk: uncertain event that can impact the project o Effect: potential consequences of the risk occurring 

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Example: A shortage of skilled Business Analysts (cause) could result in many missed requirements (risk), leading to rework or customer dissatisfaction (effect). Risk Analysis: The process of analyzing and prioritizing risk. The analyzing and prioritizing of risks is done in the Risk Management Workbook on the Risk Assessment-Mitigation tab 46

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and in the Risk Register. Risks are prioritized as High, Medium or Low. The prioritization of risks, determines other steps that may need to happen. Risk Response Planning: The process of developing options and actions to enhance opportunities and to reduce threat to project objectives. Mitigating actions are documented on the Risk Assessment and Mitigation tab in the Risk Management workbook and in the Risk Register. If a risk is prioritized as High, then mitigating actions must be documented (area is unshaded). If a risk is prioritized as Medium, then mitigating actions are recommended, but not required. If a risk is prioritized as Low, then mitigating actions are not required. 3.2 Risk Mitigation Plans Mitigating Actions to Consider o Risk Avoidance - Actions taken to eliminate the source of risk (e.g. change vendor, lower requirements, change project team member, etc.) o Risk Mitigation - Actions taken to mitigate the severity and consequences of a risk (e.g. greater training, delayed deployment, etc.) o Risk Transfer - The transfer of risk from one group to another (e.g. purchasing insurance, etc.) o Risk Monitoring - The monitoring and periodic reevaluation of a risk for changes to key risk parameters o Risk Acceptance - Acknowledging the risk but not taking preventive measures 

Risk-related change to Scope/Time/Cost The risk response planning process may result in a decision to avoid a risk by changing the project, or to mitigate a risk by taking action to lesser the probability and/or impact in the event the risk occurs. Whenever risk response planning results in potential change to the project, that change must first be requested, analyzed and approved in accordance with the project’s Change Management Plan and related processes.

Risk Monitoring and Control: The process of implementing risk response plans, tracking identified risks, monitoring residual risks, identifying new risk, and evaluating risk process effectiveness throughout the project. Monitoring Risks:Project teams should review project risks and on regular basis to determine if there are any new project risks and to determine if any actions are needed (if a risk turns to an issue). Escalation:If a overall project risk is: o Low: Project risks are low and therefore no additional review need to occur. o Medium: Project risks should be reviewed on a monthly basis by the Business Owner, Technical Owner and core project team. 

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o


High: Project risks should be reviewed on a monthly basis by the Project Sponsor and Project Steering Committee.

3.3 Estimation Techniques Estimation of software projects can be done by different techniques. The important techniques are: 1. Estimation by Expert Judgement 2. Estimation by Analogy 3. Estimation by Available Resources 4. Estimation by Software Price 5. Estimation by Parametric Modeling 3.4 Use Case Points Use Case Points are used as an analysis phase technique for estimating software development. Assuming the Business Analyst (BA) composes system use cases for describing functional requirements, the BA can use this technique for estimating the followon implementation effort. This article reviews the process of estimating the follow-on development effort for use cases. Use Case Points are derived from another software development estimating technique called “Function Points.” However, Function Points are used by systems analysts as a development phase technique that requires technical detail for estimating. – Use Case Points Conducted by business analysts during the analysis phase of a project  Can be classified as a class IV or III estimate (4)  Is based on information contained in a business requirement document  Functional requirements as modeled via system use cases, actors, and scenarios   Nonfunctional requirements Assumptions on developmental risks  Calculating Use Case Points To use this estimating technique, the business analyst uses four tables and four formulas. Tables  Two tables represent functional requirements as described by system use cases. Note  that both of these tables are entitled “unadjusted” since the totals from these tables need to be adjusted by the work entailed by nonfunctional requirements and the risk due to the characteristics of the development team. Unadjusted Actor Points  Unadjusted Scenario Points   Nonfunctional Requirements Adjustment Table 48

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SOF WARE PROCESS AND PROJECT MANAGEMENT

Developmental Risk Adjustment Table Formulas Nonfunctional Adjustment Factor Developmental Risk Adjustment Factor Total Adjustment Factor Total Unadjusted Use Case p ints Total Adjusted Use Case Poi ts Development Work Estimate excluding project management, Infrast ucture plus other supporting work, and testing – there has been some effort to inclu e testing using a similar method with historica average testing hours per use case point (5) Step 1 – Functional Require ent Tables The first table (Table 1) co siders the system use case actors. T e purpose of the Unadjusted Actor Point Table is to classify and score different types f system use case actors. System use case act r roles can be portrayed by internal systems, external systems or people/time/sensors de pending on the application. Respectivel , these role types can be classified on the Karner eighted scale: simple – 1, average – 2, complex – 3. For example: 

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Weight 1 – simple Number 2 – average Actors 3 – complex

Actor Scale

Actor Role Type

Simple

Internal System Application 1

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3

Average

External Application

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6

Complex

Person, Time, Sensor

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ystem

Total Unadjusted Actor Poi ts

o Unadjusted Points

2

Table 1. Unadjusted ctor Points The next table (Table 2) considers the system use cases. T e purpose of the Unadjusted Use Case Point Table is to classify and score use ca es on the basis of the number of dialogue exchanges. A dialogue exchange is defined as an actor request and a system response. Note that even t ough the system may have different res onses to the same actor request, for example an erro message, it is still considered a single dialogue exchange. These exchanges can be classifie on a Karner weighted scale of simple (1-3 exchanges), average (4-7 exchanges), and com lex (over 7 exchanges).

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Use Scale

Case Use Case Exchanges

Simple Average Complex


Weight 5 - simple Unadjusted Points Dialogue 10 - Number of Use (weight x number of use average Cases cases) 15 complex

1-3 dialogue exchanges 5 4-7 dialogue exchanges 10 Over 7 dialogue 15 exchanges

3 10

15 100

6

90

Total Unadjusted Scenario Points

205

Table 2. Unadjusted Use Case Points Step 2 – Nonfunctional Requirement Table The next table (Table 3) considers the nonfunctional requirements in order to adjust the system use case and actor points for application complexity. The purpose of the Nonfunctional Requirement Adjustment Factor Table is to classify and score nonfunctional requirements or general characteristics of the application. The total nonfunctional score along with the total developmental risk score (next step) is used as a factor to adjust the total unadjusted actor and use case points. The nonfunctional requirements are weighted (0-5, 5 being most concern) on importance and on a complexity scale (0-5, 0 being least complex, 3 being average, and 5 being most complex). Note that in the example, I have stated nonfunctional requirements, associated weights and complexity per my own experience; this means that the “constant” in the nonfunctional requirement adjustment factor formula needs to be determined for the average application.

Nonfunctional Characteristic

Requirement

Distributed System Performance Usability Backup and Recovery Compatibility Scalability Transition Requirements Portability Maintainability

or

Complexity Weight (0-5) Score General(0-5) 0 – least (weight 5 – most 3 – average complexity) concern 5 – most

2 1 0.5 1 1

5 5 2 2 2

10 5 1 2 2

1 0.5 2 1

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5 3 Disaster Recovery 5 Availability 4 Security 3 Training/Operability Total Nonfunctional Score 53.5 Table 3. Nonfunctional Requirement Adjustment Factor (may be augmented with columns on weight and complexity rational) Step 3 – Developmental Risk Table The next table (Table 4) considers the development risk to adjust the system use case and actor points for risk associated with the team. The purpose of the Developmental Risk Adjustment Factor Table is to classify and score perceived risk with the team in developing the application. As stated previously, the total developmental risk score along with the total nonfunctional score is used as a factor to adjust the total unadjusted actor and use case points. As with the nonfunctional requirements, the developmental risk is weighted (0-5, 5 being most concern) on importance and rated on a scale (0-5, 0 being low, 3 being average, and 5 being high). However, in the case of developmental risk, some risks are opposite in nature and should have a negative weight. For example, part-time members * (see Table 4) rather than full-time members have a negative impact. Therefore, a high rating for part-time members should have a negative score. Note that in the example, I have stated development risks, associated weights and ratings per my own experience; this means that the “constant” in the development risk adjustment factor formula needs to be determined for the average application. Reliability

Team Risk

Skill Level Experience Level OO Experience Leadership Motivation Requirement Stability *

Part-time Team Members Tool Availability

Total Development Risk Score

5 3 5 4 3

Weight (0-5) 5 – most concern

Rated (0-5) Score 0 – low (weight x impact) 3 – average 5 – high

1.5 1 1 1 1 2 -1 1

3 3 2 3 3 2 5 3

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Table 4. Developmental Risk A justment Factor (may be augmented with columns on weight and rating rational) Step 4 - The Formulas The business analyst use the first two formulas to calculate he nonfunctional adjustment and the developmental risk factors. But before we delve into the formulas, I want to encourage the rea er to understand the formulas. I recently read an article on blindly following templates (6); I feel the same way about formulas. Don’t just blindly use them. Understand how the formula works. As mentioned before, if you hange the tables (modify entries or weights), ensure you adjust the formula “const nts” so that the formulas still yield factors of 1 f r an average system.. Nonfunctional Adjustment Fact r = “a constant” + (.01*Total Nonfun tional Score) Formula derived from function point technique The higher total nonfunctional score the higher the nonfunctional ad justment factor Formula needs to yield a factor of 1 (no effect) for an average system “a constant” – in the example in this article, given the nonfunctio al requirements, weights, and average co plexity rating of 3 for all categ ries, the Total Nonfunctional Score is 45 which yields (.01*45 = .45) Therefore, “a constant” o .55 is used to provide a factor of for an average application With the constant set to .5 , the nonfunctional adjustment factor ranges from .55 to 1.3 (i.e., Total Nonfunctional Score 0 to 75 respectively) For the example in this rticle the nonfunctional adjustment factor is .55 + (.01*53.5) or 1.085 Developmental Risk Adjustment Factor = “a constant” + (-0.03*Tot l Developmental Risk Score) No equivalent formula in function point technique The higher total development risk score the lower the developmenta risk adjustment factor (i.e., multiplier is negat ve) Multiplier 0.03 is used rather than the same multiplier used in t e nonfunctional adjustment factor; the highe multiplier lowers the developmental risk adjustment factor more with a higher dev lopmental risk score Formula needs to yield a factor of 1 (no effect) for an average system “a constant” – in the ex mple in this article, given the dev lopmental risks, weights, and average co plexity rating of 3 for all categ ries, the Total Development Risk Score is 2.5 which yields (-0.03*22.5 = -.675) Therefore, “a constant” of 1.675 is used to provide a factor of 1 for an average application

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With the constant set to 1.675, the developmental risk adjustment factor ranges from 1.825 to .55 (i.e., Total Development Risk Score -5 to 37.5 respectively)

With the above adjustment factors calculated, the business analyst proceeds with final formulas for the use case point estimate. The business analyst first determines total adjustment factor by multiplying the nonfunctional and development risk factors. Given the nonfunctional requirements and developmental risks I have stated, the total adjustment factor ranges from a minimum of .3025 to a maximum of 2.735. The BA then adds up the total unadjusted scenario case and actor points, and finally multiplies the total adjustment factor and total unadjusted use case and actor points to produce the total adjusted use case points (see formulas and examples below). Total Adjustment Factor = Nonfunctional * Developmental Risk = 1.085 * 1.15 = 1.24775 Total Unadjusted Use Case Pts = unadjusted Scenario Pts + unadjusted Actor Pts = 205 + 27 = 232 Total Adjusted Use Case Pts = Total Adjusted Factor * Total unadjusted Use Case Pts = 1.24775 * 232 = 289 (rounded) The Conversion Formula With the total adjusted use case points, the business analyst can now determine the work effort estimate in units of time for developing the use cases (note caveat at the beginning of this article). The key element in this conversion is the development hours per use case point. Unfortunately, the historical average development hours per use case point are probably not available for your firm. However, averages for function points are available ranging from 15 to 45 hours per function point depending on the industry (7). These averages may be used in lieu of established use case point averages. When in doubt, practitioners recommend to use 20 hours (8). Work Effort Estimate = Total Adjusted Use Case Points * Average Hours per Use Case Point =

289

*

20

= 5780 hours or 723 days or 145 weeks or 36 months (rounded) Summary Figure 1 depicts a high-level view of the Use Case Points estimates process. As with any estimating technique, the purpose of Use Case Points is to set expectations. The 2005 Agilis 53

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Solutions and FPT Software results have provided some evidence of its accuracy. Business Analysts can use this estimating technique during the analysis phase of a project to generate class II or III estimates. Note the caveat of project management and testing exclusion, ensure the correct constant is used in the adjustment factor formulas, and limited availability of historical averages on use case points per hour.

Figure 1. Use Case Points Estimating Process 3.5 Functional Points: Function Points Conducted by systems analysts during the development phase   Can be classified as a class II or I estimate which should be more detailed (4) Is based on information contained in a technical specification  Data Functions using internal and external logical files  Transaction Functions using external inputs and external outputs, plus external inquiries  FPA Uses and Benefits in Project Planning Project Scoping A recommended approach for developing function point counts is to first functionally decompose the software into its elementary functional components (base functional components). This decomposition may be illustrated graphically on a functional hierarchy. The hierarchy provides a pictorial ‘table of contents’ or ‘map’ of the functionality of the application to be delivered. This approach has the advantage of being able to easily convey the scope of the application to the user, not only by illustrating the number of functions delivered by each functional area, but also a comparative size of each functional area measured in function points. Assessing Replacement Impact If the software to be developed is planned to replace existing production applications, it is useful to asses if the business is going to be delivered more, less or the same functionality. The replacement system’s functionality can be mapped against the functionality in the existing system. A quantitative assessment of the difference can be measured in function 54

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points. Note, this comparison can only be done if the existing applications have already been sized in function points. Assessing Replacement Cost Multiplying the size of the application to be replaced by an estimate of the dollar cost per function point to develop, enables project sponsors to develop quick estimates of replacement costs. Industry derived costs are available and provide a ballpark figure for the likely cost. Industry figures are a particularly useful reference if the re-development is for a new software or hardware platform not previously experienced by the organisation. Ideally, organisations should establish their own ‘cost per function point’ metrics for their own particular environment, based on project history. If you are considering implementing a ‘customised off the shelf’ package solution, then this provides a quick comparison of the estimated package implementation costs to compare with an in-house build. Package costs typically need to include the cost of re-engineering the business to adapt the current business processes to those delivered by the package. These costs are usually not a consideration for in-house developed software. Negotiating Scope Initial project estimates often exceed the sponsor's planned delivery date and budgeted cost. A reduction in the scope of the functionality to be delivered is often needed so that it is delivered within a predetermined time or budget constraints. The functional hierarchy provides the ‘sketch-pad’ to do scope negotiation. It enables the project manager and the user to work together to identify and flag (label) those functions which are: mandatoryfor the first release of the application; essential but not mandatory; or optional and could be held over to a subsequent release. The scope of the different scenarios can then be quickly determined by measuring the functional size of the different scenarios. For example, the project size can be objectively measured to determine what the size (and cost and duration) would be if all functions are implemented, only mandatory functions are implemented, onlymandatory and essential functions are implemented. This allows the user to make more informed decisions on which functions will be included in each release of the application, based on their relative priority compared to what is possible given the time, cost and resource constraints of the project. Evaluating Requirements Functionally sizing the requirements for the application quantifies the different types of functionality delivered by an application. The function point count assigns function points to each of the function types: External Inputs, Outputs, Enquiries, and Internal and External Files. Industry figures available from the ISBSG repository for projects measured with IFPUG function points indicate that ‘complete’ applications tend to have consistent and predictable ratios of each of the function types. The profile of functionality delivered by each of the 55

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function types in a planned application can be compared to that of the typical profile from implemented applications, to highlight areas where the specifications may be incomplete or there may be anomalies. The following pie chart illustrates the function point count profile for a planned Accounts Receivable application compared to that from the ISBGS data. The reporting functions (outputs) are lower than predicted by industry comparisons. Incomplete specification of reporting functions is a common phenomenon early in a project’s lifecycle and highlights the potential for substantial growth creep later in the project as the user identifies all their reporting needs. Estimating Project Resource Requirements Once the scope of the project is agreed, the estimates for effort, staff resources, costs and schedules need to be developed. If productivity rates (hours per function point, $cost per function point ) from previous projects are known, then the project manager can use the function point count to develop the appropriate estimates. If your organisation has only just begun collecting these metrics and does not have sufficient data to establish its own productivity rates, then the ISBSG industry data can be used in the interim. Allocating Testing Resources The functional hierarchy developed as part of the function point count during project development can assist the testing manager to identify high complexity functional areas which may need extra attention during the testing phase. Dividing the total function points for each functional area by the total number of functions allocated to that group of functions, enables the assessment of the relative complexity of each of the functional areas. The effort to perform acceptance testing and the number of test cases required is related to the number and complexity of the user functions within a functional area. Quantifying the relative size of each functional area will enable the project manager to allocate appropriate testing staff and check relative number of test cases assigned. Risk Assessment Many organisations have large legacy software applications that, due to their age, are unable to be quickly enhanced to changed business needs. Over time, these applications have been patched and expanded until they have grown to monstrous proportions. Frustrated by long delays in implementing changes, lack of support for their technical platform and expensive support costs, management will often decide to redevelop the entire application. For many organisations, this strategy of rebuilding their super-large applications has proved to be a disaster, resulting in cancellation of the project mid-development. Industry figures show that the risk of project failure rapidly increases with project size. Projects less than 500 function points have a risk of failure of less than 20% in comparison with projects over 5,000 function points which have a probability of cancellation close to 40%. This level of risk is unacceptable for most organisations.

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Assessing planned projects for their delivered size in function points enables management to make informed decisions about the risk involved in developing large, highly integrated applications or adopting a lower risk phased approach described below. Phasing Development If the project manager decides on a phased approach to the project development, then related modules may be relegated to different releases. This strategy may require temporary interfacing functionality to be built in the first release, to be later decommissioned when the next module is integrated. The function point count allows project managers to develop ‘what-if’ scenarios and quantify the project scope of each phase as a means of making objective decisions. Questions to which quantitative answers can be provided are: how much of the interfacing functionality can be avoided by implementing all of the related modules in release one? what is the best combination of potential modules to group within a release to minimise the development of temporary interfacing functions? If it is decided to implement the application as a phased development, then the size of each release can be optimised to that which is known to be manageable. This can be easily done by labelling functions with the appropriate release and performing ‘what-if’ scenarios by including and excluding functions from the scope of the count for the release. FPA Uses and Benefits in Project Construction Monitoring Functional Creep Function point analysis provides project management with an objective tool by which project size can be monitored for change, over the project’s lifecycle. As new functions are identified, functions are removed or changed during the project, the function point count is updated and the impacted functions appropriately flagged. The project scope can be easily tracked and reported at each of the major milestones. If the project size exceeds the limits allowed in the initial estimates, then this will provide an early warning that new estimates may be necessary or, alternatively, highlight a need to review the functionality to be delivered by this release. Assessing and Prioritising Rework Function Point Analysis allows the project manager to objectively and quantitatively measure the scope of impact of a change request, and to estimate the resulting impact on project schedule and costs. This immediate feedback to the user on the impact of the rework allows them to evaluate and prioritise change requests. The cost of rework is often hidden in the overall project costs, and users and developers have no means to quantify its impact on the overall project productivity rates. Function point analysis enables the project manager to measure the functions that have been reworked due to user-initiated change requests. The results provide valuable feedback to the business on the potential cost savings of committing user resources early in the project to establish an agreed set of requirements and minimising change during the project life-cycle. 

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FPA Uses and Benefits after Software Implementation Planning Support Resources and Budgets The number of personnel required to maintain and support an application is strongly related to the application’s size. Knowing the functional size of the application’s portfolio allows management to confidently budget for the deployment of support resources. For example, within an Australian financial organisation the average maintenance assignment scope (number of function points supported per person) is 833 function points per person. The assignment scope has been found to be negatively influenced by the age of the application and the number of users, i.e. as both these parameters increase the assignment scope decreases. Capers Jones figures show similar assignment scopes where for ageing, unstructured applications with high complexity, an assignment scope of 500 function points per person is not unusual, whereas for newer, structured applications, skilled staff can support around 1,500 – 2,000 function points. Once implemented, applications typically need constant enhancement in order to respond to changes in direction of an organisation’s business activities. Function points can be used to estimate the impact of these enhancements. The baseline function point count of the existing application will facilitate these estimates. As the application size grows with time, the increasing assignment scope will provide the justification to assign more support staff. Benchmarking The function point count of delivered functionality provides input into productivity and quality performance indicators. These can then be compared to those of other in-house development teams and implementation environments. Benchmarking internally and externally with industry data enables identification of best practice. External benchmarking data is readily available in the ISBSG Repository. Identifying Best Practice Project managers seeking ‘best practice’ in their software development and support areas recognise the need to adopt new tools, techniques and technologies to improve the productivity of the process and the quality of the products they produce. Baselining current practice enables management to establish current status and set realistic targets for improvement. Ongoing measurement of productivity and quality key performance indicators enable management to assess the impact of their implemented changes and to identify where further improvements can be made. Function points are the most universally accepted method to measure the output from the software process. They are a key metric within any process improvement program because of their ability to normalise data from various software development environments, combined with their ability to measure output from a business perspective as compared to a technical perspective. Planning New Releases The functional hierarchy of the functionality delivered by an application can also assist the support manager in planning and grouping change requests for each new release of the 58

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application. The hierarchy illustrates closely related functions and their relative size. If the impact of change is focused on a group of related functions, then development effort will be reduced, particularly in the design, testing and documentation stages of the project. This strategy of evaluating the scope of impact of a change request also reduces project risk by restricting projects to a manageable size and focusing change on a restricted set of related business functions. Software Asset Valuation Function Point Analysis is being used increasingly by organisations to support the ‘valuation of their software assets’. In the past, software has been considered an expense rather than a capital asset and, as such, was not included in an organisation's asset register. The most commonly used software valuation method is based on the ‘deprival method’. This method values the software based on what it would cost to replace in today’s technical environment, rather than what it cost originally to build. The industry build rate (dollar cost per function point) is determined and the total replacement value is calculated based on the current functional size of the application. Since FPA provides a means of reliably measuring software, then some organisations have implemented accrual budgeting and accounting in their business units. Under this directive, all assets must be valued based on deprival value and brought to account, thus ensuring better accountability of the organisation's financial spending. Funding via budget allocation is based on assets listed in their financial accounts and their depreciation. In the past, the purchase price of the software was recorded as an expense within an accounting year. These more recent accounting practices mean that it can now be valued as an asset and depreciated. Publicly listed organisations have found that by using this accrual accounting method of measuring software as an asset rather than an expense they can amortise the depreciation over five years rather than artificially decrease the current year’s profit by the total cost of the software. This strategy has a dramatic effect on their share price because their software is listed as a capital asset, so contributes to the overall worth of the company. The total cost of that asset has a reduced impact on the current year’s reported profit. Outsourcing Software Production and Support The benefits of functional size measurement in outsourcing contracts, is that functional size enables suppliers to measure the cost of a unit of output from the IT process to the business, and enables them to negotiate on agreed outcomes with their client.Specifically, these output based metrics based on function point analysis have enabled suppliers to: quantitatively and objectively differentiate themselves from their competitors quantify extent of annual improvement and achievement of contractual targets negotiate price variations with clients based on an agreed metric measure financial performance of the contract based on unit cost of output at contract renewal, be in a stronger bargaining position supported by an eststablished set of metrics 

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Conversely, these output based metrics based on function point analysis have enabled clients to: objectively assess supplier performance based on performance outputs delivered rather than concentrating on inputs consumed. establish quantitative performance targets and implement supplier penalties and bonuses based on achievement of these targets measure the difference between internal IT costs compared to the cost of outsourcing based on similar output quantitatively compare competing suppliers at contract tender evaluation stage. Many international outsourcing companies use function point based metrics as part of their client service level agreements. Whilst this method of contract management is relatively new, its proponents are strong supporters of the usefulness of the technique. In our experience once an outsourcing contract has been based on function point metrics, subsequent contract renewals expand on their use. Metrics initiatives have a high cost and need substantial investment, which is often overlooked at contract price negotiation. Both the supplier and the client typically incur costs. However, given the size of the penalties and bonuses associated with these contracts, it soon becomes obvious that this investment is necessary. 

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Customising Packaged Software Background For selected MIS applications, implementing a packaged ‘off the shelf’ solution is the most cost effective and time efficient strategy to deliver necessary functionality to the business. All of the benefits and uses of Function Point Analysis which applied to in-house development projects as described in the previous section can also apply to projects which tailor a vendor supplied package to an organisation's specific business needs. Experience shows that Function Point Counting of packages is not always as straightforward as sizing software developed in-house, for the following reasons: only the physical and technical functions are visible to the counter. the logical user view is often masked by the physical implementation of the original logical user requirements. in most cases the functional requirements, functional specifications, and logical design documentation are not delivered with the software. The counter may have to rely on the User Manual or online help to assist in interpreting the user view. The modelling of the logical business transactions often requires the function point counter to work with the client to identify the logical transactions. They do this by investigating the user's functional requirements and interpreting the logical transactions from the package’s physical implementation. 

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In most cases, the names of the logical files accessed by the application’s transactions are not supplied by the package vendor. The function point counter will need to develop the data model by analysing the data items processed by the application. However, with sufficient care, a reasonably accurate function point count of packaged applications can usually be obtained. Estimating Package Implementations The estimates for a package solution need to be refined for each implementation depending on the percentage of the functionality which is: native to the package and implemented without change functionality within the package which needs to be customised for this installation functionality contained with the organisation's existing applications which needs to be converted to adapt to the constraints of the package to be built as new functions in addition to the package functions to be built to as new functions to enable interfacing to other in-house applications not to be delivered in this release. The productivity rates for each of these different development activities (to implement, customise, enhance or build) are usually different. This complexity of assigning an appropriate productivity factor can be compounded when the package provides utilities which enable quick delivery based on changes to rule tables. Change requests, which can be implemented by changing values in rule-based tables, can be implemented very efficiently compared to a similar user change request that requires source code modification. It is recommended that these different types of activities are identified, and effort collected against them accordingly so that productivity rates for the different activity types can by determined. The functions can be flagged for their development activity type and their relative contributions to the functional size calculated. This will enable fine-tuning of the project estimates. Another area of concern when developing estimates for package integration is the need to determine the extent that the application module needs to interface with existing functionality. The function point count measures the external files accessed by transactions within this application. A high percentage of interface files (>10%) suggests a high degree of coupling between this application and existing applications. A high degree of interfacing tends to have a significant negative impact on productivity rates and needs to be considered when developing estimates. 

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3.6 COCOMO II COCOMO I

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In this section the model of COCOMO I also called COCOMO'81 is presented. The underlying software lifecyle isa waterfall lifecycle. Detailed information about the ratings as well as the cost drivers can be found in Boehm proposed three levels of the model: basic, intermediate, detailed. The basic COCOMO'81 model is a single-valued, static model that computes software development effort (and cost) as a function of program size expressed in estimated thousand delivered source instructions (KDSI). The intermediate COCOMO'81 model computes software development effort as a function of program size and a set of fifteen "cost drivers" that include subjective assessments of product, hardware, personnel, and project attributes. The advanced or detailed COCOMO'81 model incorporates all characteristics of the intermediate version with an assessment of the cost driver’s impact on each step (analysis, design, etc.) of the software engineering process. COCOMO'81 models depends on the two main equations 1. development effort: MM = a * KDSI b based on MM - man-month / person month / staff-month is one month of effort by one person. In COCOMO'81, there are 152 hours per Person month. According to organization this values may differ from the standard by 10% to 20%. 2. effort and development time(TDEV) : TDEV = 2.5 * MM c The coefficents a, b and c depend on the mode of the development. There are three modes of development: Table 1: development modes 

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Development Mode Size 1.Organic Small 2.Semi-detached Medium 3.Embedded Large

Project Characteristics Innovation Deadline/constraints Dev. Environment Little Not tight Stable Medium Medium Medium Greater Tight Complex hardware/ customer interfaces

1.1 BASIC COCOMO

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The basic COCOMO applies the parameterised equation without much detailed consideration of project characteristics. Basic COCOMO a b c MM = a * KDSI b Organic 2.4 1.05 0.38 Semi-detached 3.0 1.12 0.35 Embedded 3.6 1.20 0.32 TDEV=2.5 * MM c Overview 1: COCOMO I; equations and parameters of the basic COCOMO I INTERMEDIATE COCOMO The same basic equation for the model is used, but fifteen cost drivers are rated on a scale of 'very low' to 'veryhigh' to calculate the specific effort multiplier and each of them returns an adjustment factor which multipliedyields in the total EAF (Effort Adjustment Factor). The adjustment factor is 1 for a cost driver that's judged asnormal.In addition to the EAF, the model parameter "a" is slightly different in Intermediate COCOMO from the basicmodel. The parameter "b" remains the same in both models. Intermediate COCOMO a b c MM = a * KDSI b Organic 3.2 1.05 0.38 Semi-detached 3.0 1.12 0.35 Embedded 2.8 1.20 0.32 TDEV=2.5 * MM c Overview 2: COCOMO I; equations and parameters of the intermediate COCOMO IMan Month correction is now: Equation 1: intermediate COCOMO; man month correction Note: Only the Intermediate form has been implemented by USC in a calibrated software tool. ADVANCED, DETAILED COCOMO The Advanced COCOMO model computes effort asa function of program size and a set of cost drivers weighted according to each phase of the softwarelifecycle. The Advanced model applies theIntermediate model at the component level, andthen a phase-based approach is used toconsolidate the estimate [Fenton, 1997].The four phases used in the detailed COCOMOmodel are: requirements planning and productdesign (RPD), detailed design (DD), code and unittest (CUT), and integration and test (IT). Each costdriver is broken down by phases as in the exampleshown in Table 2. Table 2: Analyst capability effort multiplier for detailed COCOMO

Cost Driver Rating RPD DD ACAP Very Low 1.80 1.35 Low 0.85 0.85

CUT 1.35 0.85

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Nominal 1.00 1.00 1.00 1.00 High 0.75 0.90 0.90 0.85 Very High 0.55 0.75 0.75 0.70 Estimates for each module are combined into subsystems and eventually an overall project estimate. Using thedetailed cost drivers, an estimate is determined for each phase of the lifecycle. ADA COCOMO COCOMO has been continued to evolve and improve since its introduction. Beneath the "ADA COCOMO" model:The model named "ADA_87" assumes that the ADA programming language is being used. The use of ADA madeit easier to develop highly reliable systems and to manage complex applications. The APM_88 model is basedupon a different "process model" called the ADA Process Model. Among the assumptions are: ADA is used to produce compiler checked package specifications by the Product Design Review (PDR) Small design teams are used More effort is spent in requirements analysis and design Less effort is spent in coding, integration, and testing 

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EXAMPLE OF INTERMEDIATE COCOMO Required: database system for an office automationproject. Project = organic (a=3.2, b = 1.05, c=0.38), 4 modules to implement: data entry 0.6 KDSI MM Korr = (1.15*1.06*1.13*1.17) * 3.2 * 3.0 1.05 data update 0.6 KDSI MM Korr = 1.61* 3.2*3.17 query 0.8 KDSI MM Korr = 16.33 report generator 1.0 KDSI TDEV = 2.5*16.33 0.38 System SIZE 3.0 KDSI TDEV = 7.23 (>7months to complete) Efforts are rated as follows (all others nominal, 1.0): How many people should be hired? MM Korr / TDEV = team members 16.33 / 7.23 = 2.26 (>2 team members) cost drivers level EAF complexity high 1.15 storage high 1.06 experience low 1.13 prog capabilities low 1.17 ADVANTAGES OF COCOMO'81 64

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COCOMO is transparent, one can see how it works unlike other models such as SLIM Drivers are particularly helpful to the estimator to understand the impact of different factors that affect project costs DRAWBACKS OF COCOMO'81 It is hard to accurately estimate KDSI early on in the project, when most effort estimates are required KDSI, actually, is not a size measure it is a length measure Extremely vulnerable to mis-classification of the development mode Success depends largely on tuning the model to the needs of the organization, using historical datawhich is not always available COCOMO II At the beginning an overall context is given where the need of the reengineering of COCOMO I is stressed aswell as the focused issues for the new COCOMO version are presented. REENGINEERING COCOMO I NEEDS new software processes new phenomena: size, reuse need for decision making based on incomplete information FOCUSED ISSUES The reengineering process focussed on issues such as: 1. non-sequential and rapid-development process models 2. reuse-driven approaches involving commercial-off-the-shelf (COTS) packages 3. reengineering [reused, translated code integration] 4. applications composition 5. application generation capabilities 6. object oriented approaches supported by distributed middleware 7. software process maturity effects 8. process-driven quality estimation STRATEGY 1. Preserve the openness of the original COCOMO 2. Key the structure of COCOMO II to the future software marketplace sectors 3. Key the inputs and outputs of the COCOMO II submodels to the level of information available 4. Enable the COCOMO II submodels to be tailored to a project's particular process strategy (early prototyping stage [application composition model], Early Design stage, post-architecture stage  

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COCOMO II provides a family (COCOMO suite) of increasingly detailed software cost estimation models, eachtuned to the sectors' needs and type of information available to support software cost estimation THREE PRIMARY PREMISES PARTICULAR PROCESS DRIVERS: current and future software projects tailor their processes to their particularprocess drivers (no single, preferred software life cycle model anymore), process drivers include COTS/reusable software availability, degree of understanding requirements and architectures and otherschedule constraints as size and required reliability INFORMATION: Consistency between granularity of the software model and the information available RANGE ESTIMATES: coarse-grained cost driver information in the early project stages, and increasingly finegrainedinformation in later stages. Not point estimates of software cost and effort, but rather range estimates tied to the degree of definition of the estimation inputs. DIFFERENCES BETWEEN COCOMO I AND COCOMO II The major differences between COCOMO I AND COCOMO II are: COCOMO'81 requires software size in KDSI as an input, but COCOMO II is based on KSLOC (logicalcode). The major difference between DSI and SLOC is that a single Source Line of Code may beseveral physical lines. For example, an "if-then-else" statement would be counted as one SLOC, butmight be counted as several DSI. COCOMO II addresses the following three phases of the spiral life cycle: applications development,early design and post architecture COCOMO'81 provides point estimates of effort and schedule, but COCOMO II provides likely ranges ofestimates that represent one standard deviation around the most likely estimate. The estimation equation exponent is determined by five scale factors (instead of the three development modes) Changes in cost drivers are: Added cost drivers (7): DOCU, RUSE, PVOL, PLEX, LTEX, PCON, SITE Deleted cost drivers (5): VIRT, TURN, VEXP, LEXP, MODP Alter the retained ratings to reflect more up-do-date software practices Data points in COCOMO I: 63 and COCOMO II: 161 COCOMO II adjusts for software reuse and reengineering where automated tools are used for translation of existing software, but COCOMO'81 made little accommodation for these factors COCOMO II accounts for requirements volatility in its estimates 

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COCOMO II MODEL DEFINITION COCOMO II provides three stage series of models for estimation of software projects: APPLICATION COMPOSITION MODEL for earliest phases or spiral cycles [prototyping, and any other prototypingoccurring later in the life cycle]. EARLY DESIGN MODEL for next phases or spiral cycles. Involves exploration of architectural alternatives orincremental development strategies. Level of detail consistent with level of information available and thegeneral level of estimation accuracy needed at this stage. POST-ARCHITECTURE MODEL: once the project is ready to develop and sustain a fielded system it should have alife-cycle architecture, which provides more accurate information on cost driver inputs, and enables moreaccurate cost estimates. DEVELOPMENT EFFORT ESTIMATES SIZING A good size estimate is very important for a good model estimate. However determining size can be very challenging because projects are generally composed of new, reused (with or without modifications) and automatically translated code. The baseline size in COCOMO II is a count of new lines of code. SLOC is defined such that only source lines that are DELIVERED as part of the product are included – testdrivers and other support software (-as long as they aren't documented as carefully as source code- isexcluded).One SLOC is one logical source statement of code (e.g. declarations are counted, comments not)In COCOMO II effort is expressed as person months (PM). Person month is the amount of time one personspends working on the software development project for one month. Code size is expressed in thousands ofsource lines of code (KSLOC). The goal is to measure the amount of intellectual work put into programdevelopment. Counting source lines of Code (SLOC) takes account of new lines of code (reused code has to beadjusted). There are two possibilities: either to count the source lines of code (with the Software EngineeringInstitute (SEII) checklists or tool supported (see 10 Tools p. 19)) or to count the unadjusted function points andto convert them via "backfiring" tables to source lines of code. For further information on counting theunadjusted function points (UFP) as well as the ratios for the conversion can be found in and THE SCALE FACTORS Most significant input to the COCOMO II model is size. Size is treated as a special cost driver in that it has anexponential factor, E. This exponent is an aggregation of five scale factors. They are only used at the projectlevel for both the Early Design and the PostArchitecture model. All scale factors have qualitative rating levels('extra low' to 'extra high'). 67

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Each scale factor is the subjective weighted average of its characteristics. Theratings can be fountat The five Scale Factors are: 1. PREC Precedentedness (how novel the project is for the organization) 2. FLEX Development Flexibility 3. RESL Architecture / Risk Resolution 4. TEAM Team Cohesion 5. PMAT Process Maturity Scale Factors (Wi) annotation PREC If a product is similar to several previouslydeveloped project, then the precedentednessis high FLEX Conformance needs with requirements /external interface specifications,…Describe much the same influencesthat the original Development Mode d id, largely intrinsic to a project anduncontrollableRESL Combines Design Thoroughness and RiskElimination (two scale factors in Ada). TEAM accounts for the sources of project turbulenceand entropy because of difficulties insynchronizing the project's stakeholders. PMAT time for rating: project start. Two ways forrating: 1. by the results of an organized evaluation based on the SEI CMM, 2. 18 KeyProcess Areas in the SEI CMM.Identify management controllablesby which projects can reducediseconomies of scale by reducingsources of project turbulence,entropy and rework. COCOMO II MODELING METHODOLOGY Preparation includes: Parameter definition, how to use parameters to produce effective estimates. The methodology tries to minimize risk of lost expert time and maximize estimates. Here the question arises whichparameters are significant (most estimate efficient), in which way and with which rating scale. Seven modeling steps 1. analyse existing literature 2. review software cost modelling literature 3. to insight on improved functional forms potentially significant parameters 4. parameter definition issues (e.g. size) 5. identification of potential new parameters Process Maturity and Multisite Development 6. continuation of a number of parameters from COCOMO I 7. dropping of such COCOMO I parameters as turnaround time and modern programming practices (subsumed by process maturity) The Bayesian approach was used for COCOMO II and is (will be) reused for COCOTS, COQUALMO, COPSEMOand CORADMO. THE ROSETTA STONE 68

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The ROSETTA STONE is a system that updates COCOMO I so that it can be used with COCOMO II models. The ROSETTA STONE permits users to translate project files from COCOMO 81 to COCOMO II (backwardscompatibility). EMERGING EXTENSIONS Because of the rapid changes in software engineering development not all directions of impact could take place in the COCOMO II model. So some emerging extensions were required to overcome the deficiencies. Allof them are complementary to the COCOMO II model and some of them are still experimental, their calibrationand counting rules aren't robust enough till now. Further research still has to be done.In the following section they are presented briefly. For further particulars please contact the list of references. The discussed extensions are: estimating the cost of software COTS integration (COCOTS) Application Composition Model phase distributions of schedule and effort (COPSEMO) rapid application development effort and schedule adjustments (CORADMO) quality in terms of delivered defect density (COQUALMO) effects of applying software productivity strategies /improvement (COPROMO) System Engineering (COSYSMO) 3.7 Top Down and Bottom Up Estimation There are two approaches to estimating unit costs: top-down, bottom-up, which can be combined to form a ‘mixed approach’. Generally, a bottom-up approach is used to estimate the costs of service usage whereas top-down costing is more amenable to estimating the society level costs which are often intangible and where data is scarce. Top-down unit cost estimation The top-down approach is based on a simple calculation: divide total expenditure (quantum of funding available) for a given area or policy by total units of activity (e.g. patients served) to derive a unit cost. The units of activity are specific to the services that are being costed, for example the cost of a prison place, GP consultation, or social work assessment. Typically this approach uses aggregate, budgetary data to estimate a unit cost. The advantages of the top-down approach are: Availability of data: the availability of budgetary data means that top-down approaches can be applied easily; Simplicity: the calculation required to estimate unit costs is easy to understand and direct, providing a simple way to quantify the administrative and overhead costs associated with a range of public services and Low cost: the availability of aggregate cost data means that the time and costs required to estimate a top-down unit cost are minimal.       

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There are, however, two main limitations associated with a top-down approach. First, it does not identify what drives costs and therefore often masks the underlying factors that determine why unit costs vary within a single yet heterogeneous group of service users - for example, children in care. Second, top-down costing cannot be used to reliably forecast how costs might rise or fall as a result of changes in that way that people use services (e.g. the intensity, duration of service usage) or how costs might change due to improvements in outcomes. Therefore, using top-down unit costs may incorrectly estimate the savings from SIB interventions. Bottom-up unit cost estimation The bottom-up approach provides a greater level of granularity than the top-down method. It involves identifying all of the resources that are used to provide a service and assigning a value to each of those resources. These values are summed and linked to a unit of activity to derive a total unit cost – this provides a basis for assessment of which costs can be avoided as a result of reduced demand. The advantages of using a bottom-up approach are: Transparency: detailed cost data allows potential errors to be investigated and their impact tested – this facilitates the quality assurance process; Granularity: detailed cost data can highlight variations in cost data, and enable practitioners to explore the drivers of variation and determine whether, for example, some service users account for a disproportionate share of costs; and Versatility: the methodology enables a practitioner to forecast how costs may change as a result of a reduction in service usage or demand. Estimating efficiency savings through a bottom-up approach to unit cost estimation is a more robust method of estimating benefits to the commissioner, particuarly those related to time savings and reductions in demand for services. However, the main disadvantage associated with the bottom-up approach is that it is labour intensive; the cost, time and expertise require to apply it may be prohibitive for providers. The strengths and weaknesses of top-down and bottom-up unit cost estimation are summarised in the table below. In practice, a combined approach may be most practicable for SIB developers due to time constraints and the availability of cost data. Regardless of which approach is used, the costs avoided through a SIB should take into account how individuals (or groups of individuals) use a service. Service users are heterogeneous and their service usage will reflect this - both in intensity and duration and this will impact on the costs of service provision. Costs should therefore consider the specific cohort to be targeted by a SIB intervention. 

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Granularity/Transparency

High

Low

Credibility

High

Low

Ease of exploring variation in costs High

Low

Cost of data collection

Medium-High

Low

Data requirements

High: requires detailed, local Low: budgetary data that is often data is often unavailable available

Level of Approximation (e.g. assumptions) Low-Medium

Medium-High

Forecasting (changes in cost following the introduction or redesign of a service) Medium-High

Low

3.8 Work Breakdown Structure A work breakdown structure (WBS), in project management and systems engineering, is a deliverable-oriented decomposition of a project into smaller components. A work breakdown structure element may be a product, data, service, or any combination thereof. A WBS also provides the necessary framework for detailed cost estimating and control along with providing guidance for schedule development and control.

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1. 2. 3. 4.


The scope of the project, "deliverables" of the project. Start and end time of the scope of project. Budget for the scope of the project. Name of the person related to the scope of project.

. Example from MIL-HDBK-881, which illustrates the first three levels of a typical aircraft system.[11] Defense Materiel Item categories from MIL-STD-881C are: Aircraft Systems WBS Electronic Systems WBS Missile Systems WBS Ordnance Systems WBS Sea Systems WBS Space Systems WBS Surface Vehicle Systems WBS Unmanned Air Vehicle Systems WBS Unmanned Maritime Systems WBS Launch Vehicle Systems WBS Automated Information Systems WBS The common elements identified in MIL-STD-881C, Appendix L are: Integration, assembly, test, and checkout; Systems engineering; Program management; System test and evaluation; Training; Data; Peculiar support equipment; Common support equipment; Operational/Site activation; Industrial facilities; Initial spares and repair parts. The standard also includes 

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additional common elements unique to Space Systems, Launch Vehicle Systems and Automated Information Systems. In 1987, the Project Management Institute (PMI) documented the expansion of these techniques across non-defense organizations. The Project Management Body of Knowledge(PMBOK) Guide provides an overview of the WBS concept, while the "Practice Standard for Work Breakdown Structures" is comparable to the DoD handbook, but is intended for more general application.[12] 3.9 Macro and Micro Plans • The Macro or Top-Down approach can provide a quick but rough estimate – Done when the time and expense of a detailed estimate are an issue – Usually occurs during conception stage when a full design and WBS are not available – Requires experienced personnel to do the estimate – Can be highly inaccurate • A Micro or Bottom-Up approach can provide a fairly accurate estimate, but is time consuming – Takes into account the project design and a “roll-up” of WBS elements – May require multiple personnel and time to complete – If done properly, a bottom-up estimate can yield accurate cost and time estimates Steps to developing the estimates • Start with a Macro estimate then refine with a Micro estimate • Develop the general project definition • Perform a macro cost and time estimate • Develop the detailed project definition and WBS • Roll-up the WBS elements as part of a micro estimate • Establish the project schedules • Reconcile differences between the macro and micro estimates Macro Estimates • Scaling:Given a cost for a previous project then an estimate for a new project can be scaled from the known cost. E.g NASA, at times, uses spacecraft weight to estimate total cost. • Apportion:Given a similar previous project, costs for major subunits of the new project would be proportional to similar subunits in the previous project. • Weighted Variables:Certain types of projects can be characterized by specific parameters (e.g. number of inputs, number of detector channels). Historical costs & times for single units of these parameters are weighted by the numbers required for the new project. 73

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• Learning Curve:If the same task is repeated a number of times there will be a cost / time savings relative to the first time the task is done. Micro Estimates • Template:Uses historical data to establish detailed costs and schedules for project subunits. A new project composed of some combination of these subunits can then be quickly estimated. • Ratio:Similar to the Macro ratio method but applied to specific tasks associated with project subunits. For example, if it takes 1 day to build & test a particular sensor unit, then an instrument with 10 sensors would take 2 technicians, 5 days to complete. WBS Roll-up:Times and costs associated with the lowest level WBS work packages are estimated and then these are added or rolled-up to yield the costs for higher level units. This method provides the most accurate estimates at the expense of time devoted to developing the estimate 3.10 Planning Poker Planning poker, also called Scrum poker, is a consensus-based technique for estimating, mostly used to estimate effort or relative size of development goals in software development. In planning poker, members of the group make estimates by playing numbered cards facedown to the table, instead of speaking them aloud. The cards are revealed, and the estimates are then discussed. By hiding the figures in this way, the group can avoid the cognitive bias of anchoring, where the first number spoken aloud sets a precedent for subsequent estimates. Planning poker is a variation of the Wideband Delphi method. It is most commonly used in agile software development, in particular the Scrum and Extreme Programmingmethodologies. Process The reason The reason to use Planning poker is to avoid the influence of the other participants. If a number is spoken, it can sound like a suggestion and influence the other participants' sizing. Planning poker should force people to think independently and propose their numbers simultaneously. This is accomplished by requiring that all participants show their card at the same time. Equipment Planning poker is based on a list of features to be delivered, several copies of a deck of cards and optionally, an egg timer that can be used to limit time spent in discussion of each item. The feature list, often a list of user stories, describes some software that needs to be developed. The cards in the deck have numbers on them. A typical deck has cards showing the Fibonacci sequence including a zero: 0, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89; other decks use similar progressions. 74

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Planning Poker card deck The reason for using the Fibo acci sequence is to reflect the inhe ent uncertainty in estimating larger items.Several co mercially available decks use the sequ nce: 0, ½, 1, 2, 3, 5, 8, 13, 20, 40, 100, and option lly a ? (unsure) and a coffee cup (I ne d a break). Some organizations use standard playin cards of Ace, 2, 3, 5, 8 and King. W ere King means: "this item is too big or too compl cated to estimate." "Throwing a King" nds discussion of the item for the current sprint. Smartphones allow developers to use apps instead of physical card decks. When teams are not in the same geographical locations,collaborative software can be used s replacement for physical cards. Procedure At the estimation meeting, each stimator is given one deck of the cards. All decks have identical sets of cards in them. The meeting proceeds as follows: A Moderator, who will not pla , chairs the meeting. The Product Manager provides a short overview. The team is given an opportunity to ask questions and discuss to clari assumptions and risks. A summary o the discussion is recorded by the Project Manag r. Each individual lays a card fa e down representing their estimate. Units used vary - they can be days duration, ideal da s or story points. During discussion, nu bers must not be mentioned at all in relation to feature size to avoid anchoring. Everyone calls their cards sim ltaneously by turning them over. People with high estimates and low estimates are given a soap ox to offer their ustification for their estimate and then discussion continues. Repeat the estimation process ntil a consensus is reached. The develo er who was likely to own the deliverable has a large portion of the "consensus vote", although the Moderator can negotiate the consensus. To ensure that discussion is structured; the Moderator or the Project M nager may at any point turn over the egg timer and when it runs out all discussion must cease and another round of poker is played. The structure in the conversation is re-intro uced by the soap boxes. 

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The cards are numbered as they are to account for the fact that the longer an estimate is, the more uncertainty it contains. Thus, if a developer wants to play a 6 he is forced to reconsider and either work through that some of the perceived uncertainty does not exist and play a 5, or accept a conservative estimate accounting for the uncertainty and play an 8. Planning poker benefits Planning poker is a tool for estimating software development projects. It is a technique that minimizes anchoring by asking each team member to play their estimate card such that it cannot be seen by the other players. After each player has selected a card, all cards are exposed at once. A study by Moløkken-Østvold and Haugen[6] found that [the] set of control tasks in the same project, estimated by individual experts, achieved similar estimation accuracy as the planning poker tasks. However, for both planning poker and the control group, measures of the median estimation bias indicated that both groups had unbiased estimates, as the typical estimated task was perfectly on target. 3.11Wideband Delphi The Wideband Delphi estimation method is a consensus-based technique for estimating effort. It derives from the Delphi method which was developed in the 1950-1960s at theRAND Corporation as a forecasting tool. It has since been adapted across many industries to estimate many kinds of tasks, ranging from statistical data collection results to sales and marketing forecasts.

Wideband Delphi Process Barry Boehm and John A. Farquhar originated the Wideband variant of the Delphi method in the 1970s. They called it "wideband" because, compared to the existing delphi method, the new method involved greater interaction and more communication between those participating. The method was popularized by Boehm's book Software Engineering Economics (1981). Boehm's original steps from this book were: 1. Coordinator presents each expert with a specification and an estimation form. 2. Coordinator calls a group meeting in which the experts discuss estimation issues with the coordinator and each other. 3. Experts fill out forms anonymously. 4. Coordinator prepares and distributes a summary of the estimates 5. Coordinator calls a group meeting, specifically focusing on having the experts discuss points where their estimates vary widely 6. Experts fill out forms, again anonymously, and steps 4 to 6 are iterated for as many rounds as appropriate. A variant of Wideband Delphi was developed by Neil Potter and Mary Sakry of The Process Group. In this process, a project manager selects a moderator and an estimation team with 76

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three to seven members. The Delphi process consists of two meetings run by the moderator. The first meeting is the kickoff meeting, during which the estimation team creates a work breakdown structure (WBS) and discusses assumptions. After the meeting, each team member creates an effort estimate for each task. The second meeting is the estimation session, in which the team revises the estimates as a group and achieves consensus. After the estimation session, the project manager summarizes the results and reviews them with the team, at which point they are ready to be used as the basis for planning the project. Choose the team. The project manager selects the estimation team and a moderator. The team should consist of 3 to 7 project team members. The team should include representatives from every engineering group that will be involved in the development of the work product being estimated. Kickoff meeting. The moderator prepares the team and leads a discussion to brainstorm assumptions, generate a WBS and decide on the units of estimation. Individual preparation. After the kickoff meeting, each team member individually generates the initial estimates for each task in the WBS, documenting any changes to the WBS and missing assumptions. Estimation session. The moderator leads the team through a series of iterative steps to gain consensus on the estimates. At the start of the iteration, the moderator charts the estimates on the whiteboard so the estimators can see the range of estimates. The team resolves issues and revises estimates without revealing specific numbers. The cycle repeats until either no estimator wants to change his or her estimate or the estimators agree that the range is acceptable. Assemble tasks. The project manager works with the team to collect the estimates from the team members at the end of the meeting and compiles the final task list, estimates and assumptions. Review results. The project manager reviews the final task list with the estimation team. 

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3.12 Documenting the Plan To foster a successful planning phase, here are seven planning documents 1.Project management plan -- This is used as a reference index, encompassing all planning and project documents. 2. High-level project schedule plan -- This document captures high-level project phases and key milestones. It is the document most project stakeholders will see or want to see. 3. Project team planning -- This document provides a "who-is-doing-what" view of the project. This document fosters efficient project execution and effective project communication. 4. Scope plan -- The scope plan documents the project requirements, the agreed scope and the RequirementsTraceabilityMatrix (RTM) summary.

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5. Detailed project work plan -- This keeps track of the activities, work packages, resources, durations, costs, milestones, project's critical path, etc. It will be an essential document and work guideline for your coreprojectteam. 6. Quality assurance planning - This document tracks the quality stan ards your project deliverables will have to align to. These may typically include product testing approach and tools, quality policies, quality c ecklists, deviations definitions, quality metrics, product defect severity grades, acceptance criteria,costof poor quality, etc. 7. Risk planning -- This document contains the project risks and the related mitigation plans; as well as the project oppor unities and the related exploiting plans. he importance of this document is one of the most underestimated in project planning. Be repared to have a contingency plan in case somethi g goes wrong or to take advantage of o pportunities when they arise. Start with this checklist when you sit down to plan for your next project-planning phase. Depending on your proje t's needs, fine tune the checklist and ailor it by adding and removing planning assets, determining the planning time frame, the nderlying details and rigor. Revisit this planning exercise, le rn from it and enhance it, to continuo sly improve your project planning skills. 3.13 Tracking the Plan Project tracking

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It is helpful to see an example of project tracking that does not incl de earned value performance management. Consi er a project that has been planned in etail, including a time-phased spend plan for all elements of work. Figure 1 shows the cumulative budget (cost) for this project as a functi n of time (the blue line, labeled PV). It also shows the

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cumulative actual cost of the pro ect (red line) through week 8. To those unfamiliar with EVM, it might appear that this roject was over budget through week 4 and then under budget from week 6 through week 8. However, what is missing from this chart is any understanding of how much wor has been accomplished during the project. If the project was actually completed at week 8 then the project would actually be well under budget and well ahead of schedule. If, on the ther hand, the project is only 10% complete at week 8, the project is significantly over budget and behind schedule. A method is needed to measure technical performance objectively and quantitatively, and that is what EV accomplishes. Project tracking with EVM Consider the same project, except this time the project plan includes pre-defined methods of quantifying the accomplishmen of work. At the end of each eek, the project manager identifies every detailed lement of work that has been completed, and sums the PV for each of these completed eleme ts. Earned value may be accumulated monthly, weekly, or as progress is made. Earned value (EV)

Figure 2 shows the EV curve (in green) along with the PV curve from F gure 1. The chart indicates that technical performa ce (i.e., progress) started more rapidly than planned, but slowed significantly and fell behind schedule at week 7 and 8. This c art illustrates the schedule performance aspect o EVM. It is complementary to critical path or critical chain schedule management. Figure 3 shows the same EV curve (green) with the actual cost data from Figure 1 (in red). It can be seen that the project wa actually under budget, relative to the amount of work accomplished, since the start of t e project. This is a much better conclusion than might be derived from Figure 1. Figure 4 shows all three curves t gether – which is a typical EVM line c art. The best way to read these three-line charts is o identify the EV curve first, then com pare it to PV (for schedule performance) and AC (for cost performance). It can be seen from this illustration that a true understanding of co t performance and schedule performa ce relies first on measuring technical performance objectively. This is the foundational prin iple of EVM. 3.14 Earned Value Method (EV ) Earned value managemen (EVM), pro ect/performance or Earned value management (EVPM) is a project management technique for measuring project performance and progress in an o jective manner. Earned value management is a project management technique for easuring project performance and progress. It has t e ability to combine measurements of: Scope Schedule, and 

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Costs In a single integrated system, Earned Value Management is able to provide accurate forecasts of project performance problems, which is an important contribution for project management. Early EVM research showed that the areas of planning and control are significantly impacted by its use; and similarly, using the methodology improves both scope definition as well as the analysis of overall project performance. More recent research studies have shown that the principles of EVM are positive predictors of project success.[1] Popularity of EVM has grown significantly in recent years beyond government contracting, in which sector its importance continues to rise[2] (e.g., recent new DFARS rules), in part because EVM can also surface in and help substantiate contract disputes.[4] Essential features of any EVM implementation include 1. a project plan that identifies work to be accomplished, 2. a valuation of planned work, called Planned Value (PV) or Budgeted Cost of Work Scheduled (BCWS), and 3. pre-defined “earning rules” (also called metrics) to quantify the accomplishment of work, called Earned Value (EV) or Budgeted Cost of Work Performed (BCWP). EVM implementations for large or complex projects include many more features, such as indicators and forecasts of cost performance (over budget or under budget) and schedule performance (behind schedule or ahead of schedule). However, the most basic requirement of an EVM system is that it quantifies progress using PV and EV. As a short illustration of one of the applications of the EVM consider the following example. Project A has been approved for duration of 1 year and with the budget of X. It was also planned, that after 6 months project will spend 50% of the approved budget. If now 6 months after the start of the project a Project Manager would report that he has spent 50% of the budget, one can initially think, that the project is perfectly on plan. However in reality the provided information is not sufficient to come to such conclusion, as from one side within this time project can spend 50% of the budget, whilst finishing only 25% of the work (which would mean project is not doing well), similarly a project can spend 50% of the budget, whilst completing 75% of the work (which would mean, that project is doing better, than planned). EVM' is meant to address such and similar issues. 

UNIT IV CONFIGURATION AND QUALITY MANAGEMENT 4.1 Identifying Artifacts to be Configured Identifying the artifacts that make up an application The artifacts that make up a typical Endeca application definition include the following: 81

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The AppConfig.xml file The AppConfig.xml file describes each of the application's provisioning information and is stored in the EAC Central Server. The Deployment Template control scripts use AppConfig.xml as the authoritative source for application definition. The Deployment Template stores a copy of the AppConfig.xml file in the [appdir]/config/script directory. Although you can modify an application configuration with the Workbench, we recommend that modifications only be made in the AppConfig.xml file. That way, the application configuration will be saved on disk, ready for sharing between environments. You can use the Workbench for other tasks that do not involve modifying the configuration, such as reviewing the configuration, and starting or stopping individual components. Note: Some parts of the AppConfig.xml file include settings that are environment specific, such as the application's name, file system paths, and host addresses in the environment. These settings should be collected and stored in a custom file. For more information about how to create this file, see the topic about Creating a custom file for environment-specific settings. The instance configuration The instance configuration is a set of files that control the ITL process and the data loaded into the MDEX Engine servers. The instance configuration files are controlled by the Developer Studio, and optionally by the Workbench. These files include configuration data such as dimension definition, search configuration, the Forge pipeline, and Page Builder landing pages. Page Builder templates Page Builder templates are used to drive dynamic landing pages that can be created in Page Builder. They are defined by xml files stored by the Workbench, and accessed through the emgr_update command utility. Command-line scripts An application deployment typically includes command-line scripts that perform common operations related to the application's functionality. By convention, these scripts are stored in the Deployment Template's [appdir]/controldirectory. The Deployment Template includes scripts such as baseline_update and set_baseline_data_ready_flag. You can create additional scripts under the [appdir]/control directory. These scripts, together with their input data and output directories, are a part of the application definition. For example, a developer might create scripts to crawl web pages in preparation for a baseline update. These scripts might take as input a seed-list file, and create an output file in a custom directory under [appdir]. These command-line scripts, along with their input data and output directories, should be shared among the development, staging and production environments. Library files 82

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Many parts of an application use library files. For example, a Forge pipeline using a Java or Perl manipulator typically requires access to library files implementing those manipulators. BeanShell scripts may use application- or Endeca-specific Java classes. By convention, library files are kept under [appdir]/config/lib. Forge state files Forge state files reside in the [appdir]/data/state directory. In most cases these files do not need to be included as part of an application definition. However, when an application uses dimension values from auto-generated or external dimensions, then Forge state files do need to be synchronized across the environments. In this situation, the state files contain the IDs of these dimension values and ensure that the same dimension value always gets the same ID no matter how many times Forge is run. These dimension values may be used in a variety of ways, including dynamic business rules, landing pages, dimension ordering, and precedence rules. In other words, Forge state files should be identified as part of the application definition if the application uses auto-generated dimensions or external dimensions, and values from these dimensions are referenced anywhere in the application configuration (for example, in dynamic business rules, Page Builder landing pages, explicit dimension ordering, or in precedence rules). 4.2 Naming Conventions and Version Control This process describes the deliverable types, naming conventions and version control mechanisms to be applied to deliverables produce by the project. The following table describes the configurable item types used within the IFS project. Configuration Application Naming Convention Version Item Type Used Control Business Area: 5 characters Manual – via Document - MS Word Specifications (e.g. HR or CeDICT) version Project: 4-6 characters (e.g. numbering Tavern, OHSW) Deliverable Type :2 or 3 characters UT = Unit Task Specification PM = Project Management deliverable TPR = Test Plan & Result CR = Change Request PF = Process Flow OC = Organisation chart RR = Resource Request 83

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Configuration Application Naming Convention Item Type Used PR = Presentations PS = Project Standard MN = Minutes AD = Architecture Deliverable AF = Acceptance Form DR = Deliverable Review Form DI = Diagram ST = Strategy document Description: Brief description of deliverable Version : character followed by major and minor numbering, OR Date in yymmdd format. This version format is used for deliverables that are to be kept at a point in time. File Type : as per application Reviews: If deliverable review comments and changes are provided within the deliverable itself, i.e. via Track Changes, the reviewer’s initials are added to the file name. Example: HR OHSW-PM QualityPlan v0.1.doc Review Example: HR OHSW -PM QualityPlan v0.1 PW.doc Document – MS Word As above Test Plans

Document – MS Word Test Results

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Configuration Application Naming Convention Item Type Used

Version Control numbering Document – MS Word As above Manual – via Project version Management numbering Document – MS Word As Above Manual – via Resource date Request versioning Document – MS Word As Above Manual – via Meeting date Minutes versioning Presentations MS Power As Above Manual – via Point version numbering Process Visio As above Manual – via Diagrams version numbering Organisation Visio As Above Manual – via Charts date versioning Logs and Excel As Above Manual – via Registers Register held within project date Status Report versioning Change MS Word As Above Manual – via Requests date versioning Risks MS Word As Above Manual – via version numbering Issues MS Word As Above Manual – via version numbering Defects TBC Assignment MS Word As Above Manual – via Descriptions date versioning Work MS Project Descriptive name, e.g. HR Manual – via Schedules or Excel date OHSW -yyyymmdd 85

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Configuration Application Naming Convention Item Type Used

Version Control versioning

Manual Version Control If the deliverable version is controlled by the date in the deliverable file name then for each new version of the deliverable the current date is used in the file name. If there are multiple version created for the same deliverable on the same day then an alphabetic character is appended to the date starting at ‘a’.

If the deliverable version is controlled by major and minor numbering the following process is followed: 1. The Original draft of the deliverable is versioned using zero as the major number and 1 as the minor number, i.e. v0.1 2. For each revision following internal reviews the minor number is incremented, i.e. v0.1 becomes v0.2 and then v0.3 etc. 3. Once the deliverable has completed internal review and is to be distributed to the business representatives for review minor number is incremented. 4. For each revision pertaining to business representatives reviews the minor number is incremented. 5. Following business representative review the deliverable is updated and when ready for acceptance the major number is set to 1 and the minor number set to zero. 6. If the deliverable requires a revision due to changes identified in the acceptance process the major number remains unchanged and the minor number is incremented. 7. The version numbering for changes to deliverables after acceptance follow the same process except that the starting number is the deliverable accepted version number. Upon completion of reviews the major number is incremented by one and the minor number is set to zero, e.g. v1.6 becomes v2.0. Automated Version Control Approval Cycle

Role Name Reviewer(s ):

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Date

Approver(s ):

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Version (State) 0.1

Author

Change Description

Date

Peter Woolley

Original draft

21/61/20 13

4.3 Configuration Control Configuration control is an important function of the configuration management discipline. Its purpose is to ensure that all changes to a complex system are performed with the knowledge and consent of management. The scope creep that results from ineffective or nonexistent configuration control is a frequent cause of project failure. Configuration control tasks include initiating, preparing, analysing, evaluating and authorising proposals for change to a system (often referred to as "the configuration"). Configuration control has four main processes: 1. Identification and documentation of the need for a change in a change request 2. Analysis and evaluation of a change request and production of a change proposal 3. Approval or disapproval of a change proposal 4. Verification, implementation and release of a change.

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The Configuration Control Process

Why Configuration Control is Important Configuration control is an essential component of a project's risk management strategy. For example, uncontrolled changes to software requirements introduce the risk of cost and schedule overruns. Scenario - Curse of the Feature Creep A project misses several key milestones and shows no sign of delivering anything. WHY? The customer regularly talks directly to software developers asking them to make 'little changes' without consulting the project manager. The developers are keen to show off the new technology they are using. They slip in the odd 'neat feature' that they know the customer will love. Solution: Implement configuration control. Document all requests for change and have them considered by aConfiguration Control Board. 4.4 Quality Assurance Techniques SQA Software Quality Assurance (SQA) consists of a means of monitoring thesoftware engineering processes and methods used to ensure quality. It does thisby means of audits of the quality management system under which the softwaresystem is created. These audits are backed by one or more standards, usually ISO 9000.It is distinct from software quality control which includes reviewing requirementsdocuments, and software testing. SQA encompasses the entire softwaredevelopment process, which includes processes such as software design, 

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coding,source code control, code reviews, change management, configuration management,and release management. Whereas software quality control is a control of products, software quality assurance is a control of processes. Software quality assurance is related to the practice of quality assurance inproduct manufacturing. There are, however, some notable differences betweensoftware and a manufactured product. These differences stem from the factthat the manufactured product is physical and can be seen whereas the softwareproduct is not visible. Therefore its function, benefit and costs are not as easily measured. What’s more, when a manufactured product rolls off the assemblyline, it is essentially a complete, finished product, whereas software is never finished.Software lives, grows, evolves, and metamorphoses, unlike its tangiblecounterparts. Therefore, the processes and methods to manage, monitor, andmeasure its ongoing quality are as fluid and sometimes elusive as are the defects that they are meant to keep in check. SQA is also responsible for gathering and presenting software metrics. For example the Mean Time Between Failure (MTBF) is a common softwaremetric (or measure) that tracks how often the system is failing. This SoftwareMetric is relevant for the reliability software characteristic and, by extension theavailability software characteristic.SQA may gather these metrics from various sources, but note the importantpragmatic point of associating an outcome (or effect) with a cause. In this waySQA can measure the value or consequence of having a given standard process, or procedure. Then, in the form of continuous process improvement, feedbackcan be given to the various process teams (Analysis, Design, Coding etc.) anda process improvement can be initiated. Overview of methods Software Quality Assurance takes several forms. A brief list of testing methodsthat should be considered Methods: • Black box testing - not based on any knowledge of internal design or code.Tests are based on requirements and functionality. • White box testing - based on knowledge of the internal logic of an application’scode. Tests are based on coverage of code statements, branches,paths, conditions • Unit testing - the most ’micro’ scale of testing; to test particular functionsor code modules. Typically done by the programmer and not by testers,as it requires detailed knowledge of the internal program design and code.Not always easily done unless the application has a welldesigned architecture with tight code; may require developing test driver modules or testharnesses• Incremental integration testing - continuous testing of an application asnew functionality is added; 89

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requires that various aspects of an application’sfunctionality be independent enough to work separately before all parts of the program are completed, or that test drivers be developed as needed;done by programmers or by testers • Integration testing - testing of combined parts of an application to determineif they function together correctly. The ’parts’ can be code modules,individual applications, client and server applications on a network, etc.This type of testing is especially relevant to client/server and distributedsystems • Functional testing - black-box type testing geared to functional requirementsof an application; this type of testing should be done by testers.This doesn’t mean that the programmers shouldn’t check that their codeworks before releasing it (which of course applies to any stage of testing) • System testing - black-box type testing that is based on overall requirementsspecifications; covers all combined parts of a system • End-to-end testing - similar to system testing; the ’macro’ end of the testscale; involves testing of a complete application environment in a situationthat mimics real-world use, such as interacting with a database, usingnetwork communications, or interacting with other hardware, applications,or systems if appropriate Sanity (Smoke) testing - typically an initial testing effort to determine ifa new software version is performing well enough to accept it for a majortesting effort. For example, if the new software is crashing systems every5 minutes, bogging down systems to a crawl, or corrupting databases, thesoftware may not be in a ’sane’ enough condition to warrant further testingin its current state• Regression testing - re-testing after fixes or modifications of the softwareor its environment. It can be difficult to determine how much re-testingis needed, especially near the end of the development cycle. Automatedtesting tools can be especially useful for this type of testing• Acceptance testing -final testing based on specifications of the end-useror customer, or based on use by end-users/customers over some limitedperiod of time • Load testing - testing an application under heavy loads, such as testing ofa web site under a range of loads to determine at what point the system’sresponse time degrades or fails • Stress testing - term often used interchange eably with ’load’ and ’performance’testing. Also used to describe such tests as system functionaltesting while under unusually heavy loads, heavy repetition of certain actionsor inputs, input of large numerical values, large complex queries to adatabase system, etc. • Performance testing - term often used interchangeably with ’stress’ and’load’ testing. Ideally ’performance’ testing (and any other ’type’ of testing)is defined in requirements documentation or QA or Test Plans • Usability testing - testing for ’user-friendliness’. Clearly this is subjective,and will depend on the targeted end-user or customer. User interviews,surveys, video recording of user 90

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sessions, and other techniques can be used.Programmers and testers are usually not appropriate as usability testers • Install/Uninstall testing - testing of full, partial, or upgrade install/uninstallprocesses • Recovery testing - testing how well a system recovers from crashes, hardwarefailures, or other catastrophic problems • Failover testing - typically used interchangeably with ’recovery testing’ • Security testing - testing how well the system protects against unauthorizedinternal or external access, willful damage, etc; may require sophisticated testing techniques testing how well software performs in a • Compatibility testing particularhardware/software/operating system/network/etc. environment • Exploratory testing - often taken to mean a creative, informal softwaretest that is not based on formal test plans or test cases; testers may belearning the software as they test it • Ad-hoc testing - similar to exploratory testing, but often taken to meanthat the testers have significant understanding of the software before testingit • Context-driven testing - testing driven by an understanding of the environment,culture, and intended use of software. For example, the testingapproach for life-critical medical equipment software would be completelydifferent than that for a low-cost computer game • User acceptance testing - determining if software is satisfactory to an enduseror customer • Comparison testing - comparing software weaknesses and strengths to competingproducts • Alpha testing -testing of an application when development is nearing completion;minor design changes may still be made as a result of such testing.Typically done by end-users or others, not by programmers or testers • Beta testing - testing when development and testing are essentially completedand final bugs and problems need to be found before final release.Typically done by end-users or others, not by programmers or testers • Mutation testing - a method for determining if a set of test data or testcases is useful, by deliberately introducing various code changes (’bugs’)and retesting with the original test data/cases to determine if the ’bugs’ aredetected. Proper implementation requires large computational resources 4.5 Peer Reviews Peer Reviews and CMMI • Does not dictate specific techniques, but instead requires that: – A written policy about peer reviews is required – Resources, funding, and training must be provided – Peer reviews must be planned – The peer review procedures to be used must be documented SEI-CMMI Checklist for Peer Reviews • Are peer reviews planned? 91

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• Are actions associated with defects that are identified during peer reviews tracked until they are resolved? • Does the project follow a written organizational policy for performing peer reviews? • Do participants of peer reviews receive the training required to perform their roles? • Are measurements used to determine the status of peer review activities? • Are peer review activities and work products subjected to Software Quality Assurance review and audit? Peer review is a traditional organizational function designed to contribute to improving the quality of care and appropriate utilization of health care resources. Quality Management in CMMI 1.3 Process Areas Configuration Management (CM)

Product Integration (PI)

Causal Analysis and Resolution (CAR)

Project Monitoring and Control (PMC)

Decision Analysis and Resolution (DAR)

Project Planning (PP)

Integrated Project Management (IPM)

Quantitative Project Management (QPM)

Measurement and Analysis (MA)

Requirements Development (RD)

Organizational (OPM)

Performance

Management Requirements (REQM)

Management

Organizational Process Definition (OPD)

Risk Management (RSKM)

Organizational Process Focus (OPF)

Supplier Agreement Management (SAM)

Organizational Process Performance (OPP)

Technical Solution (TS)

Organizational Training (OT)

Validation (VAL)

Process and Product Quality Assurance Verification (VER) (PPQA)

PP QA Pro duc t and Pro ces s Qu alit y Ass ura nce

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VER - Verification

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VAL – Validation

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Peer Review • Reviews performed by peers in the development team – Can be from Fagan’s inspections to simple buddy checks – Peer Review Items – Participants / Roles – Schedule 4.6 Fegan Inspection Researchers and Influencers • Fagan • Johnson • Ackermann • Gilb and Graham • Weinberg • Weigers Inspection, Walkthrough or Review? An inspection is ‘a visual examination of a software product to detect and identify software anomalies, including errors and deviations from standards and specifications’ A walkthrough is ‘a static analysis technique in which a designer or programmer leads members of the development team and other interested parties through a software product, and the participants ask questions and make comments about possible errors, violation of development standards, and other problems’ A review is ‘a process or meeting during which a software product is presented to project personnel, managers, users, customers, user representatives, or other interested parties for comment or approval’ Families of Review Methods

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Method Family

Typical Goals

Typical Attributes

Walkthroughs

Minimal overhead Developer training Quick turnaround

Little/no preparation Informal process No measurement Not FTR!

Technical Reviews

Requirements elicitation Ambiguity resolution Training

Formal process Author presentation Wide range of discussion

Inspections

Detect and remove all defects efficiently and effectively

Formal process Checklists Measurements Verify phase

Informal vs. Formal • Informal – Spontaneous – Ad-hoc – No artifacts produced • Formal – Carefully planned and executed – Reports are produced In reality, there is also a middle ground between informal and formal techniques Cost-Benefit Analysis • Fagan reported that IBM inspections found 90% of all defects for a 9% reduction in average project cost • Johnson estimates that rework accounts for 44% of development cost • Finding defects, finding defects early and reducing rework can impact the overall cost of a project Cost of Defects What is the impact of the annual cost of software defects in the US? $59 billion 98

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Estimated that $22 billion could be avoided by introducing a best-practice defect detection infrastructure • Gilb Gilb pro proje ject ct wit with h jet jet man manuf ufac actu ture rer r • Initial analysis analysis estimated estimated that 41,000 41,000 hours of of effort would be lost lost through through faulty faulty requirements • Manu Manufac factu ture rerr con concu curre rred d beca becaus use: e: – 10 people on the project using 2,000 hours/year hours/year – Project is already one year late (20,000 hours) – Project is estimated to take one more year (another 20,000 hours) hours) Software Inspections Why are software inspections not widely used? • Lack of time • Not Not see seen n as as a prio priori rity ty • Not seen as value value added added (measu (measured red by loc) loc) • Lack of unde understa rstandi nding ng of of forma formaliz lized ed tech techniq niques ues • Im Impr prop oper er too tools ls use used d to to coll collec ectt data data • Lack Lack of of trai traini ning ng of part partici icipa pant ntss • Pits Pits pro progr gram amm mer aga again inst st rev revie iewer werss Twelve Reasons Conventional Reviews are Ineffective 1. The reviewe reviewers rs are swa swampe mped d with with inform informati ation. on. 2. Most reviewers reviewers are are not familiar familiar with the product product design design goals. goals. 3. There are no clear individual individual respon responsibil sibilities ities.. 4. Reviewers Reviewers can avoid avoid potential potential embarrass embarrassment ment by saying saying nothing. nothing. 5. The review is a large large meeting; meeting; detailed detailed discussions discussions are difficult. difficult. 6. Presence Presence of managers managers silences silences criticism criticism.. 7. Presence Presence of uninformed uninformed reviewers reviewers may may turn the review review into a tutorial. tutorial. 8. Specialists Specialists are aske asked d general general questions. questions. 9. Generalists Generalists are are expected expected to know know specifics specifics.. 10.The review procedure reviews code without respect to structure. 11.Unstated assumptions are not questioned. 12.Inadequate time is allowed. Fagan’s Contributions • Design and code code inspec inspections tions to reduce reduce errors errors in program program develop development ment (1976) (1976) • A systema systematic tic and and efficient efficient approach approach to improving improving programm programming ing quality quality • Continuous Continuous improvem improvement: ent: reduce initial initial errors and follow-up follow-up with additiona additionall improvements • Beginn Beginning ingss of form formali alized zed soft software ware inspec inspecti tions ons

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Fagan’s Six Major Steps 1. Planning 2. Overvi rview 3. Prep Prepar arat atio ion n 4. Ex Exam amin inat atio ion n 5. Rework 6.Follow-up

1. 2. 3. 4. 5. 6.

Planning: Form team, assign roles Overview: Inform team about product (optional) Prep Prepar arat atio ion: n: Independent review of materials Exam Examin inat atio ion: n: Inspection meeting Rework: Author verify defects and correct Foll Follow ow-u -up: p: Moderator checks and verifies corrections

Fagan’s Fagan’s Team Roles • Fagan Fagan recomm recommend endss that that a good good size size team consis consists ts of four four peop people le • Mod Moderat erator: or: the the key person, person, mana manages ges team team and offer offerss leadersh leadership ip • Read Readers ers,, rev review iewer erss and and auth author orss – Designer: programmer responsible for producing the the program design – Coder/ Implementer: translates the design to code – Tester: write, execute test cases

Common Inspection Processes

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4.7 Unit , Integration , System , and Acceptance Testing Testing methods Static vs. dynamic testing There are many approaches approaches available available in software testing. testing. Reviews, Reviews, walkthroug walkthroughs, hs, or inspections inspections are referred to as static testing testing,, whereas actually actually executin executing g programmed programmed code code with a given set of test cases cases is referred to as dy dynamic namic testing. testing. Static Static testing testing is often implicit implicit,, as proofreading, plus when programming tools/text editors check source code structure or compilers (pre-compilers) check syntax and data flow as static program analysis. Dynamic Dynamic testing takes place when the program itself is run. Dynamic testing may begin before the program is 100% complete in order to test particular sections of code and are applied to discrete discrete functions functions or modules. modules. Typical Typical techniques techniques for for this are either using using stubs/driv stubs/drivers ers or execution execution from a debugger debugger environm environment. ent. Static testing involves involves verification verification,, whereas dynamic testing involves involves validation. validation. Together Together they help improv improvee software software quality quality.. Among the techni techniques ques for for static static analysi analysis, s, mutation mutation testing can be used to ensure ensure the test-cases will detect errors which are introduced by mutating the source code. The box approach Software testing methods are traditionally divided into white- and black-box testing. These two approaches are used to describe the point of view that a test engineer takes when designing test cases. White-Box testing Main article: White-box testing White-box testing (also known as clear box testing, glass box testing, transparent box testing and structural testing) tests internal structures or workings of a program, as opposed to the functionality exposed to the end-user. In white-box testing an internal perspective of 101

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the system, as well as programmi g skills, are used to design test cases. he tester chooses inputs to exercise paths through the code and determine the appropriat outputs. This is analogous to testing nodes in a cir uit, e.g. in-circuit testing (ICT). While white-box testing can be applied at the unit, integration and system levels of the software testing process, it is usually done at the unit level. It can test p ths within a unit, paths between units during integr tion, and between subsystems during a system–level test. Though this method of test design can uncover many errors or problems, it might not detect unimplemented parts of the specification or missing requirements. Techniques used in white-box test ng include: API testing (application programming interface) – testing of the application using public and private APIs Code coverage – creating test to satisfy some criteria of code coverage (e.g., the test designer can create tests to cause all statements in the program to be executed at least once) Fault injection methods – inte tionally introducing faults to gauge the efficacy of testing strategies Mutation testing methods Static testing methods Code coverage tools can evaluate the completeness of a test suite that wa created with any method, including black-box testing. This allows the software team to e amine parts of a system that are rarely tested and nsures that the most important function points have been [23] tested. Code coverage as a soft are metric can be reported as a percenta e for: Function coverage, whi h reports on functions executed Statement coverage, which reports on the number of lines execut d to complete the test 100% statement coverage ens res that all code paths or branches (i terms of control flow) are executed at least once. This is helpful in ensuring correct functionality, but not sufficient since the same code ay process different inputs correctly or incorrectly. Black-box testing Main article: Black-box testin 

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Black box diagram Black-box testing treats the s ftware as a "black box", examining functionality without any knowledge of internal i plementation. The testers are only a are of what the software is supposed to o, not how it does it. Black-box testing methods include: equivalence partitio ing, boundary value analysis, all-pairs testing, state transition tables, decision table testing, fuzz testing, model-ba ed testing, use case testing, exploratory testin and specification-based testing.

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Specification-based testing aims to test the functionality of software according to the applicable requirements.[25] This level of testing usually requires thorough test cases to be provided to the tester, who then can simply verify that for a given input, the output value (or behavior), either "is" or "is not" the same as the expected value specified in the test case. Test cases are built around specifications and requirements, i.e., what the application is supposed to do. It uses external descriptions of the software, including specifications, requirements, and designs to derive test cases. These tests can be functional or non-functional, though usually functional. Specification-based testing may be necessary to assure correct functionality, but it is insufficient to guard against complex or high-risk situations. One advantage of the black box technique is that no programming knowledge is required. Whatever biases the programmers may have had, the tester likely has a different set and may emphasize different areas of functionality. On the other hand, black-box testing has been said to be "like a walk in a dark labyrinth without a flashlight."Because they do not examine the source code, there are situations when a tester writes many test cases to check something that could have been tested by only one test case, or leaves some parts of the program untested. This method of test can be applied to all levels of software testing: unit, integration, system and acceptance. It typically comprises most if not all testing at higher levels, but can also dominate unit testing as well. Visual testing The aim of visual testing is to provide developers with the ability to examine what was happening at the point of software failure by presenting the data in such a way that the developer can easily ﬁnd the information he or she requires, and the information is expressed clearly. At the core of visual testing is the idea that showing someone a problem (or a test failure), rather than just describing it, greatly increases clarity and understanding. Visual testing therefore requires the recording of the entire test process – capturing everything that occurs on the test system in video format. Output videos are supplemented by realtime tester input via picture-in-a-picture webcam and audio commentary from microphones. Visual testing provides a number of advantages. The quality of communication is increased dramatically because testers can show the problem (and the events leading up to it) to the developer as opposed to just describing it and the need to replicate test failures will cease to exist in many cases. The developer will have all the evidence he or she requires of a test failure and can instead focus on the cause of the fault and how it should be fixed.

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Visual testing is particularly well-suited for environments that deploy agile methods in their development of software, since agile methods require greater communication between testers and developers and collaboration within small teams.[citation needed ] Ad hoc testing and exploratory testing are important methodologies for checking software integrity, because they require less preparation time to implement, while the important bugs can be found quickly. In ad hoc testing, where testing takes place in an improvised, impromptu way, the ability of a test tool to visually record everything that occurs on a system becomes very important. Visual testing is gathering recognition in customer acceptance and usability testing, because the test can be used by many individuals involved in the development process. For the customer, it becomes easy to provide detailed bug reports and feedback, and for program users, visual testing can record user actions on screen, as well as their voice and image, to provide a complete picture at the time of software failure for the developer. Grey-box testing Main article: Gray box testing Grey-box testing (American spelling: gray-box testing) involves having knowledge of internal data structures and algorithms for purposes of designing tests, while executing those tests at the user, or black-box level. The tester is not required to have full access to the software's source code. Manipulating input data and formatting output do not qualify as grey-box, because the input and output are clearly outside of the "black box" that we are calling the system under test. This distinction is particularly important when conducting integration testing between two modules of code written by two different developers, where only the interfaces are exposed for test. However, tests that require modifying a back-end data repository such as a database or a log file does qualify as grey-box, as the user would not normally be able to change the data repository in normal production operations.Grey-box testing may also include reverse engineering to determine, for instance, boundary values or error messages. By knowing the underlying concepts of how the software works, the tester makes betterinformed testing choices while testing the software from outside. Typically, a grey-box tester will be permitted to set up an isolated testing environment with activities such as seeding a database. The tester can observe the state of the product being tested after performing certain actions such as executing SQL statements against the database and then executing queries to ensure that the expected changes have been reflected. Grey-box testing implements intelligent test scenarios, based on limited information. This will particularly apply to data type handling, exception handling, and so on.

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Testing levels There are generally four recognized levels of tests: unit testing, integration testing, system testing, and acceptance testing. Tests are frequently grouped by where they are added in the software development process, or by the level of specificity of the test. The main levels during the development process as defined by the SWEBOK guide are unit-, integration-, and system testing that are distinguished by the test target without implying a specific process model.[32] Other test levels are classified by the testing objective. Unit testing Unit testing, also known as component testing, refers to tests that verify the functionality of a specific section of code, usually at the function level. In an object-oriented environment, this is usually at the class level, and the minimal unit tests include the constructors and destructors. These types of tests are usually written by developers as they work on code (white-box style), to ensure that the specific function is working as expected. One function might have multiple tests, to catch corner cases or other branches in the code. Unit testing alone cannot verify the functionality of a piece of software, but rather is used to ensure that the building blocks of the software work independently from each other. Unit testing is a software development process that involves synchronized application of a broad spectrum of defect prevention and detection strategies in order to reduce software development risks, time, and costs. It is performed by the software developer or engineer during the construction phase of the software development lifecycle. Rather than replace traditional QA focuses, it augments it. Unit testing aims to eliminate construction errors before code is promoted to QA; this strategy is intended to increase the quality of the resulting software as well as the efficiency of the overall development and QA process. Depending on the organization's expectations for software development, unit testing might include static code analysis, data flow analysis, metrics analysis, peer code reviews, code coverage analysis and other software verification practices. Integration testing Integration testing is any type of software testing that seeks to verify the interfaces between components against a software design. Software components may be integrated in an iterative way or all together ("big bang"). Normally the former is considered a better practice since it allows interface issues to be located more quickly and fixed.Integration testing works to expose defects in the interfaces and interaction between integrated components (modules). Progressively larger groups of tested software components corresponding to elements of the architectural design are integrated and tested until the software works as a system.

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Component interface testing The practice of component interface testing can be used to check the handling of data passed between various units, or subsystem components, beyond full integration testing between those units.[35][36] The data being passed can be considered as "message packets" and the range or data types can be checked, for data generated from one unit, and tested for validity before being passed into another unit. One option for interface testing is to keep a separate log file of data items being passed, often with a timestamp logged to allow analysis of thousands of cases of data passed between units for days or weeks. Tests can include checking the handling of some extreme data values while other interface variables are passed as normal values.[35] Unusual data values in an interface can help explain unexpected performance in the next unit. Component interface testing is a variation of black-box testing, with the focus on the data values beyond just the related actions of a subsystem component. System testing System testing, or end-to-end testing, tests a completely integrated system to verify that it meets its requirements.For example, a system test might involve testing a logon interface, then creating and editing an entry, plus sending or printing results, followed by summary processing or deletion (or archiving) of entries, then logoff. In addition, the software testing should ensure that the program, as well as working as expected, does not also destroy or partially corrupt its operating environment or cause other processes within that environment to become inoperative (this includes not corrupting shared memory, not consuming or locking up excessive resources and leaving any parallel processes unharmed by its presence). Acceptance testing At last the system is delivered to the user for Acceptance testing. Testing Types Installation testing An installation test assures that the system is installed correctly and working at actual customer's hardware. Compatibility testing A common cause of software failure (real or perceived) is a lack of its compatibility with other application software, operating systems (or operating system versions, old or new), or target environments that differ greatly from the original (such as a terminal or GUI application intended to be run on the desktop now being required to become a web application, which must render in a web browser). For example, in the case of a lack of backward compatibility, this can occur because the programmers develop and test software only on the latest version of the target environment, which not all users may be running. This results in the unintended consequence that the latest work may not function on earlier versions of the target environment, or on older hardware that 106

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earlier versions of the target environment was capable of using. Sometimes such issues can be fixed by proactively abstracting operating system functionality into a separate program module or library. Smoke and sanity testing Sanity testing determines whether it is reasonable to proceed with further testing. Smoke testing consists of minimal attempts to operate the software, designed to determine whether there are any basic problems that will prevent it from working at all. Such tests can be used as build verification test. Regression testing Regression testing focuses on finding defects after a major code change has occurred. Specifically, it seeks to uncover software regressions, as degraded or lost features, including old bugs that have come back. Such regressions occur whenever software functionality that was previously working, correctly, stops working as intended. Typically, regressions occur as an unintended consequence of program changes, when the newly developed part of the software collides with the previously existing code. Common methods of regression testing include re-running previous sets of test-cases and checking whether previously fixed faults have re-emerged. The depth of testing depends on the phase in the release process and the risk of the added features. They can either be complete, for changes added late in the release or deemed to be risky, or be very shallow, consisting of positive tests on each feature, if the changes are early in the release or deemed to be of low risk. Regression testing is typically the largest test effort in commercial software development, due to checking numerous details in prior software features, and even new software can be developed while using some old test-cases to test parts of the new design to ensure prior functionality is still supported. Acceptance testing Acceptance testing can mean one of two things: 1. A smoke test is used as an acceptance test prior to introducing a new build to the main testing process, i.e. before integration or regression. 2. Acceptance testing performed by the customer, often in their lab environment on their own hardware, is known as user acceptance testing (UAT). Acceptance testing may be performed as part of the hand-off process between any two phases of development. Alpha testing Alpha testing is simulated or actual operational testing by potential users/customers or an independent test team at the developers' site. Alpha testing is often employed for off-theshelf software as a form of internal acceptance testing, before the software goes to beta testing.

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Beta testing Beta testing comes after alpha testing and can be considered a form of external user acceptance testing. Versions of the software, known as beta versions, are released to a limited audience outside of the programming team. The software is released to groups of people so that further testing can ensure the product has few faults or bugs. Sometimes, beta versions are made available to the open public to increase the feedback field to a maximal number of future users. Functional vs non-functional testing Functional testing refers to activities that verify a specific action or function of the code. These are usually found in the code requirements documentation, although some development methodologies work from use cases or user stories. Functional tests tend to answer the question of "can the user do this" or "does this particular feature work." Non-functional testing refers to aspects of the software that may not be related to a specific function or user action, such as scalability or other performance, behavior under certain constraints, or security. Testing will determine the breaking point, the point at which extremes of scalability or performance leads to unstable execution. Non-functional requirements tend to be those that reflect the quality of the product, particularly in the context of the suitability perspective of its users. Destructive testing Destructive testing attempts to cause the software or a sub-system to fail. It verifies that the software functions properly even when it receives invalid or unexpected inputs, thereby establishing the robustness of input validation and error-management routines.Software fault injection, in the form of fuzzing, is an example of failure testing. Various commercial non-functional testing tools are linked from the software fault injection page; there are also numerous open-source and free software tools available that perform destructive testing. Software performance testing Performance testing is generally executed to determine how a system or sub-system performs in terms of responsiveness and stability under a particular workload. It can also serve to investigate, measure, validate or verify other quality attributes of the system, such as scalability, reliability and resource usage. Load testing is primarily concerned with testing that the system can continue to operate under a specific load, whether that be large quantities of data or a large number of users. This is generally referred to as software scalability. The related load testing activity of when performed as a non-functional activity is often referred to as endurance testing.Volume testing is a way to test software functions even when certain components (for example a file or database) increase radically in size. Stress testing is a way to test reliability under unexpected or rare workloads. Stability testing (often referred to as load

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or endurance testing) checks to see if the software can continuously function well in or above an acceptable period. There is little agreement on what the specific goals of performance testing are. The terms load testing, performance testing, scalability testing, and volume testing, are often used interchangeably. Real-time software systems have strict timing constraints. To test if timing constraints are met, real-time testing is used. Usability testing Usability testing is to check if the user interface is easy to use and understand. It is concerned mainly with the use of the application. Accessibility testing Accessibility testing may include compliance with standards such as: Americans with Disabilities Act of 1990 Section 508 Amendment to the Rehabilitation Act of 1973 Web Accessibility Initiative (WAI) of the World Wide Web Consortium (W3C) Security testing Security testing is essential for software that processes confidential data to prevent system intrusion by hackers. Internationalization and localization The general ability of software to be internationalized and localized can be automatically tested without actual translation, by using pseudolocalization. It will verify that the application still works, even after it has been translated into a new language or adapted for a new culture (such as different currencies or time zones). Actual translation to human languages must be tested, too. Possible localization failures include: Software is often localized by translating a list of strings out of context, and the translator may choose the wrong translation for an ambiguous source string. Technical terminology may become inconsistent if the project is translated by several people without proper coordination or if the translator is imprudent. Literal word-for-word translations may sound inappropriate, artificial or too technical in the target language. Untranslated messages in the original language may be left hard coded in the source code. Some messages may be created automatically at run time and the resulting string may be ungrammatical, functionally incorrect, misleading or confusing. Software may use a keyboard shortcut which has no function on the source language's keyboard layout, but is used for typing characters in the layout of the target language. Software may lack support for the character encoding of the target language. 

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Fonts and font sizes which are appropriate in the source language may be inappropriate in the target language; for example, CJK characters may become unreadable if the font is too small. A string in the target language may be longer than the software can handle. This may make the string partly invisible to the user or cause the software to crash or malfunction. Software may lack proper support for reading or writing bi-directional text. Software may display images with text that was not localized. Localized operating systems may have differently named system configuration files and environment variables and different formats for date and currency. Development testing[edit] Main article: Development Testing Development Testing is a software development process that involves synchronized application of a broad spectrum of defect prevention and detection strategies in order to reduce software development risks, time, and costs. It is performed by the software developer or engineer during the construction phase of the software development lifecycle. Rather than replace traditional QA focuses, it augments it. Development Testing aims to eliminate construction errors before code is promoted to QA; this strategy is intended to increase the quality of the resulting software as well as the efficiency of the overall development and QA process. Depending on the organization's expectations for software development, Development Testing might include static code analysis, data flow analysis metrics analysis, peer code reviews, unit testing, code coverage analysis, traceability, and other software verification practices. A/B testing[edit] Main article: A/B testing Concurrent testing[edit] Main article: Concurrent testing Conformance testing or type testing[edit] Main article: Conformance testing In software testing, conformance testing verifies that a product performs according to its specified standards. Compilers, for instance, are extensively tested to determine whether they meet the recognized standard for that language. Testing process[edit] Traditional waterfall development model[edit] A common practice of software testing is that testing is performed by an independent group of testers after the functionality is developed, before it is shipped to the customer.[41]This practice often results in the testing phase being used as a project buffer to compensate for project delays, thereby compromising the time devoted to testing. [42] 

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Another practice is to start software testing at the same moment the project starts and it is a continuous process until the project finishes.[43] Further information: Capability Maturity Model Integration and Waterfall model Agile or Extreme development model[edit] In contrast, some emerging software disciplines such as extreme programming and the agile software development movement, adhere to a "test-driven software development" model. In this process, unit tests are written first, by the software engineers (often with pair programming in the extreme programming methodology). Of course these tests fail initially; as they are expected to. Then as code is written it passes incrementally larger portions of the test suites. The test suites are continuously updated as new failure conditions and corner cases are discovered, and they are integrated with any regression tests that are developed. Unit tests are maintained along with the rest of the software source code and generally integrated into the build process (with inherently interactive tests being relegated to a partially manual build acceptance process). The ultimate goal of this test process is to achieve continuous integration where software updates can be published to the public frequently. [44] [45] This methodology increases the testing effort done by development, before reaching any formal testing team. In some other development models, most of the test execution occurs after the requirements have been defined and the coding process has been completed. Top-down and bottom-up[edit] Bottom Up Testing is an approach to integrated testing where the lowest level components (modules, procedures, and functions) are tested first, then integrated and used to facilitate the testing of higher level components. After the integration testing of lower level integrated modules, the next level of modules will be formed and can be used for integration testing. The process is repeated until the components at the top of the hierarchy are tested. This approach is helpful only when all or most of the modules of the same development level are ready.[citation needed ] This method also helps to determine the levels of software developed and makes it easier to report testing progress in the form of a percentage.[citation needed ] Top Down Testing is an approach to integrated testing where the top integrated modules are tested and the branch of the module is tested step by step until the end of the related module. In both, method stubs and drivers are used to stand-in for missing components and are replaced as the levels are completed. A sample testing cycle[edit] Although variations exist between organizations, there is a typical cycle for testing.[46] The sample below is common among organizations employing the Waterfall developmentmodel. The same practices are commonly found in other development models, but might not be as clear or explicit. 111

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Requirements analysis: Testing should begin in the requirements phase of the software development life cycle. During the design phase, testers work to determine what aspects of a design are testable and with what parameters those tests work. Test planning: Test strategy, test plan, testbed creation. Since many activities will be carried out during testing, a plan is needed. Test development: Test procedures, test scenarios, test cases, test datasets, test scripts to use in testing software. Test execution: Testers execute the software based on the plans and test documents then report any errors found to the development team. Test reporting: Once testing is completed, testers generate metrics and make final reports on their test effort and whether or not the software tested is ready for release. Test result analysis: Or Defect Analysis, is done by the development team usually along with the client, in order to decide what defects should be assigned, fixed, rejected (i.e. found software working properly) or deferred to be dealt with later. Defect Retesting: Once a defect has been dealt with by the development team, it is retested by the testing team. AKA Resolution testing. Regression testing: It is common to have a small test program built of a subset of tests, for each integration of new, modified, or fixed software, in order to ensure that the latest delivery has not ruined anything, and that the software product as a whole is still working correctly. Test Closure: Once the test meets the exit criteria, the activities such as capturing the key outputs, lessons learned, results, logs, documents related to the project are archived and used as a reference for future projects. Automated testing[edit] Main article: Test automation Many programming groups are relying more and more on automated testing, especially groups that use test-driven development. There are many frameworks to write tests in, and continuous integration software will run tests automatically every time code is checked into a version control system. While automation cannot reproduce everything that a human can do (and all the ways they think of doing it), it can be very useful for regression testing. However, it does require a well-developed test suite of testing scripts in order to be truly useful. Testing tools[edit] Program testing and fault detection can be aided significantly by testing tools and debuggers. Testing/debug tools include features such as: Program monitors, permitting full or partial monitoring of program code including: Instruction set simulator , permitting complete instruction level monitoring and trace facilities 

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Program animation, permitting step-by-step execution and conditional breakpoint at source level or in machine code Code coverage reports Formatted dump or symbolic debugging, tools allowing inspection of program variables on error or at chosen points Automated functional GUI testing tools are used to repeat system-level tests through the GUI Benchmarks, allowing run-time performance comparisons to be made Performance analysis (or profiling tools) that can help to highlight hot spots and resource usage Some of these features may be incorporated into an Integrated Development Environment (IDE). Measurement in software testing[edit] Main article: Software quality Usually, quality is constrained to such topics as correctness, completeness, security,[citation needed ] but can also include more technical requirements as described under the ISOstandard ISO/IEC 9126, such as capability, reliability, efficiency, portability, maintainability, compatibility, and usability. There are a number of frequently used software metrics, or measures, which are used to assist in determining the state of the software or the adequacy of the testing. 4.8 Test Data and Test Cases Test plan A test specification is called a test plan. The developers are well aware what test plans will be executed and this information is made available to management and the developers. The idea is to make them more cautious when developing their code or making additional changes. Some companies have a higher-level document called a test strategy. Traceability matrix A traceability matrix is a table that correlates requirements or design documents to test documents. It is used to change tests when related source documents are changed, to select test cases for execution when planning for regression tests by considering requirement coverage. Test case A test case normally consists of a unique identifier, requirement references from a design specification, preconditions, events, a series of steps (also known as actions) to follow, input, output, expected result, and actual result. Clinically defined a test case is an input and an expected result. [47] This can be as pragmatic as 'for condition x your derived result is y', whereas other test cases described in more detail the input scenario and what results might be expected. It can occasionally be a series of steps (but often 

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steps are contained in a separate test procedure that can be exercised against multiple test cases, as a matter of economy) but with one expected result or expected outcome. The optional fields are a test case ID, test step, or order of execution number, related requirement(s), depth, test category, author, and check boxes for whether the test is automatable and has been automated. Larger test cases may also contain prerequisite states or steps, and descriptions. A test case should also contain a place for the actual result. These steps can be stored in a word processor document, spreadsheet, database, or other common repository. In a database system, you may also be able to see past test results, who generated the results, and what system configuration was used to generate those results. These past results would usually be stored in a separate table. Test script A test script is a procedure, or programing code that replicates user actions. Initially the term was derived from the product of work created by automated regression test tools. Test Case will be a baseline to create test scripts using a tool or a program. Test suite The most common term for a collection of test cases is a test suite. The test suite often also contains more detailed instructions or goals for each collection of test cases. It definitely contains a section where the tester identifies the system configuration used during testing. A group of test cases may also contain prerequisite states or steps, and descriptions of the following tests. Test fixture or test data In most cases, multiple sets of values or data are used to test the same functionality of a particular feature. All the test values and changeable environmental components are collected in separate files and stored as test data. It is also useful to provide this data to the client and with the product or a project. Test harness The software, tools, samples of data input and output, and configurations are all referred to collectively as a test harness. 4.9 Bug Tracking Bug tracking systems as a part of integrated project management systems[edit] Bug and issue tracking systems are often implemented as a part of integrated project management systems. This approach allows including bug tracking and fixing in a general product development process, fixing bugs in several product versions, automatic generation of a product knowledge base and release notes. Distributed bug tracking[edit] Some bug trackers are designed to be used with distributed revision control software. These distributed bug trackers allow bug reports to be conveniently read, added to the database or

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updated while a developer is offline.[3] Fossil and Veracity both include distributed bug trackers. Recently, commercial bug tracking systems have also begun to integrate with distributed version control. FogBugz, for example, enables this functionality via the source-control tool, Kiln.[4] Although wikis and bug tracking systems are conventionally viewed as distinct types of software, ikiwiki can also be used as a distributed bug tracker. It can manage documents and code as well, in an integrated distributed manner. However, its query functionality is not as advanced or as user-friendly as some other, non-distributed bug trackers such asBugzilla.[5] Similar statements can be made about org-mode, although it is not wiki software as such. Bug tracking and test management[edit] While traditional test management tools such as HP Quality Center and IBM Rational Quality Manager come with their own bug tracking systems, other tools integrate with popular bug tracking systems.[citation needed ] 4.10 Casual Analysis Causal analysis and resolution improves quality and productivity by preventing the introduction of defects or problems and by identifying and appropriately incorporating the causes of superior process performance. The Causal Analysis and Resolution process area involves the following activities: · Identifying and analyzing causes of selected outcomes. The selected outcomes can represent defects and problems that can be prevented from happening in the future or successes that can be implemented in projects or the organization. · Taking actions to complete the following: o Remove causes and prevent the recurrence of those types of defects and problems in the future o Proactively analyze data to identify potential problems and prevent them from occurring o Incorporate the causes of successes into the process to improve future process performance Reliance on detecting defects and problems after they have been introduced is not cost effective. It is more effective to prevent defects and problems by integrating Causal Analysis and Resolution activities into each phase of the project. Since similar outcomes may have been previously encountered in other projects or in earlier phases or tasks of the current project, Causal Analysis and Resolution activities are mechanisms for communicating lessons learned among projects.

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Types of outcomes encountered are analyzed to identify trends. Based on an understanding of the defined process and how it is implemented, root causes of these outcomes and future implications of them are determined. Since it is impractical to perform causal analysis on all outcomes, targets are selected by tradeoffs on estimated investments and estimated returns of quality, productivity, and cycle time. Measurement and analysis processes should already be in place. Existing defined measures can be used, though in some instances new measurement definitions, redefinitions, or clarified definitions may be needed to analyze the effects of a process change. Refer to the Measurement and Analysis process area for more information about aligning measurement and analysis activities and providing measurement results. Causal Analysis and Resolution activities provide a mechanism for projects to evaluate their processes at the local level and look for improvements that can be implemented. When improvements are judged to be effective, the information is submitted to the organizational level for potential deployment in the organizational processes. The specific practices of this process area apply to a process that is selected for quantitative management. Use of the specific practices of this process area can add value in other situations, but the results may not provide the same degree of impact to the organization’s quality and process performance objectives. UNIT V SOFTWARE PROCESS DEFINITION AND MANAGEMENT 5.1 Process Elements Accountability Each deliverables has an owner and a customer (i.e Requirements is owned by product manager. The lead architect is the customer). Each engineer or group of engineer responsible for a set of features or components should support them during Beta and early releases Team Spirit 

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Collaboration and proper diffusion of knowledge and information is key to productivity Management should intervene only in case of deviation from standards and baseline

Performance Metrics

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Overview The basic process is to define a baseline for key performance metrics following a clear but flexible set of procedures. Alerts , analysis and corrective/contingency actions should be planed when deviation from the baseline occurs. 

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Quality Coverage Each development artifacts (Requirements, (Requirements, Use Cases, Design, Design, Source Code, Test cases, Unit Black box Test, Build, Release, Usability) must have one or more quality criteria such as % approved or compliance deliverables. The overall quality index could be weighted by deliverables with higher weights with early deliverables which has more impact on the overall acceptance of the product by the customer . 

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Functionality Coverage Percentage of requirements r equirements validated by customers Percentage of user scenario validated by customers 

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Productivity Ratio of failed build, release, test cycle, reworked requirements Accuracy of initial estimation Defects turnaround time Customer escalation turnaround time 

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Activity-based Costing Average cost to fix a defect Average cost of a failed f ailed build Average cost of failed release Average cost of incorrect requirement Quality Function Deployment (QFD) 

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Probes Probes are used to collect statistics on the development process API or Web services for statistics Alerts on deviation Review & Traceability 

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Procedures

Here is a sample of procedures for the software development process: • Creating/updating coding standards • Creating/updating design guideline • Creating/updating requirements • Creating/updating use cases • Creating/updating build scripts • Creating/updating test cases • Creating/updating user documentation • Creating/updating training guide • • • • • •

Unit testing Black-box testing Defect resolution Data Integrity tests Performance testing Functional testing

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Test automation Build automation Release automation

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• • • • • • • • • • • • • •

Release Customer escalation Update Product Roadmap Build vs buy Hiring Engineering Suggestions Reusability of components Orientation new engineer Hands-on training Management by objectives Performance appraisal Source code management Process Management Project management

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Selection of offshore teams Synchronization development context with offshore teams

Automation & Tools

Here is a sample list of automation tools that can be used in software engineering • Build environment/portal • Requirements tracking system • Defect database • Test configuration management • Development environment • Test harness • Unit Test generator • Execution profiler • Code coverage analyzer 119

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Black-box automation tool Version control Collaboration platform

5.2Process Architecture 6. Process architecture is the structural design of general process systems and applies to fields such as computers (software, hardware, networks, etc.), business processes (enterprise architecture, policy and procedures, logistics, project management, etc.), and any other process system of varying degrees of complexity.[1] 7. Processes are defined as having inputs, outputs and the energy required to transform inputs to outputs. Use of energy during transformation also implies a passage of time: a process takes real time to perform its associated action. A process also requires space for input/output objects and transforming objects to exist: a process uses real space. 8. A process system is a specialized system of processes. Processes are composed of processes. Complex processes are made up of several processes that are in turn made up of several processes. This results in an overall structural hierarchy of abstraction. If the process system is studied hierarchically, it is easier to understand and manage; therefore, process architecture requires the ability to consider process systems hierarchically. Graphical modeling of process architectures is considered by Dualistic Petri nets. Mathematical consideration of process architectures may be found in CCS and the π-calculus. 9. The structure of a process system, or its architecture, can be viewed as a dualistic relationship of its infrastructure and suprastructure.[1][2] The infrastructure describes a process system's component parts and their interactions. The suprastructure considers the super system of which the process system is a part. (Suprastructure should not be confused with superstructure, which is actually part of the infrastructure built for (external) support.) As one traverses the process architecture from one level of abstraction to the next, infrastructure becomes the basis for suprastructure and vice versa as one looks within a system or without. 120

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SOF WARE PROCESS AND PROJECT MANAGEMENT [2]

10.Requirements for a process system are derived at every hierarchical evel. Black-box requirements for a system come from its suprastructure. Customer requirements are black-box requirements ne r, if not at, the top of a process archit cture's hierarchy. White-box requirements, s ch as engineering rules, programming yntax, etc., come from the process system's infrastructure. 11.Process systems are a dual stic phenomenon of change/no-change or form/transform and as such, are well-suited to being modeled by the bipartite Petri Nets modeling system and in particular, rocess-class Dualistic Petri nets where processes can be simulated in real time and s ace and studied hierarchically. 5.3 Relationship Between Elements 5.4 Process Modeling The term process model is use in various contexts. For example, in business process modeling the enterprise process model is often referred to as the business process model.

Process models are processes of the same nature that are classified together into a model. Thus, a process model is a description of a process at the type level. Since the process model is at the type level, a process is an instantiation of it. The same proc ss model is used repeatedly for the development of many applications and thus, has many i stantiations. One possible use of a process model is to prescribe how things must/should could be done in contrast to the process itself whi h is really what happens. A process m del is roughly an anticipation of what the process ill look like. What the process shall be ill be determined [2] during actual system development. The goals of a process model are t be: Descriptive Track what actually happens during a process Take the point of view of an external observer who looks at the ay a process has been performed and deter ines the improvements that must be made to make it perform more effectively or efficiently. Prescriptive Define the desired processes and how they should/could/might be performed. 

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Establish rules, guidelines, and behavior patterns which, if followed, would lead to the desired process performance. They can range from strict enforcement to flexible guidance. Explanatory Provide explanations about the rationale of processes. Explore and evaluate the several possible courses of action based on rational arguments. Establish an explicit link between processes and the requirements that the model needs to fulfill. Pre-defines points at which data can be extracted for reporting purposes. Classification of process models[edit] By coverage[edit] There are five types of coverage where the term process model has been defined differently:[3] Activity-oriented: related set of activities conducted for the specific purpose of product definition; a set of partially ordered steps intended to reach a goal.[4] Product-oriented: series of activities that cause sensitive product transformations to reach the desired product.[5] Decision-oriented: set of related decisions conducted for the specific purpose of product definition. Context-oriented: sequence of contexts causing successive product transformations under the influence of a decision taken in a context. Strategy-oriented: allow building models representing multi-approach processes and plan different possible ways to elaborate the product based on the notion of intention and strategy.[6] By alignment[edit] Processes can be of different kinds.[2] These definitions “correspond to the various ways in which a process can be modelled”. Strategic processes investigate alternative ways of doing a thing and eventually produce a plan for doing it are often creative and require human co-operation; thus, alternative generation and selection from an alternative are very critical activities Tactical processes help in the achievement of a plan are more concerned with the tactics to be adopted for actual plan achievement than with the development of a plan of achievement Implementation processes are the lowest level processes 

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are directly concerned with the details of the what and how of plan i plementation By granularity[edit] Granularity refers to the level of etail of a process model and affects the kind of guidance, explanation and trace that can b provided. Coarse granularity restricts these to a rather limited level of detail whereas fin granularity provides more detailed cap bility. The nature [2] of granularity needed is dependent on the situation at hand. Project manager, customer repre entatives, the general, top-level, or middle management require rather coarse-grained process description as they want to gain an overview of time, budget, and resource planning f r their decisions. In contrast, software engineers, users, testers, analysts, or software syste architects will prefer a fine-grained pr cess model where the details of the model can p ovide them with instructions and im ortant execution dependencies such as the depende cies between people. While notations for fine-grained odels exist, most traditional process odels are coarsegrained descriptions. Process mo els should, ideally, provide a wide ra ge of granularity [2][7] (e.g. Process Weaver). By flexibility[edit] 

[8]

Flexibility of Method construction approaches It was found that while process models were prescriptive, in actual practice departures from [6] the prescription can occur. Thus, frameworks for adopting methods evolved so that systems development methods match spe ific organizational situations and ther by improve their usefulness. The development o such frameworks is also called Situational Method Engineering. Method construction approaches can be organized in a flexibility spect um ranging from [8] 'low' to 'high'. Lying at the 'low' end of this spectrum are rigid methods, whereas at the ' igh' end there are modular method construction. Rigid methods are completely pre-defined and leave little scope for adapting them to the sit ation at hand. On the other hand, modular methods can be modified and augmented to fit a given situation. Selecting a rigid methods allows each project to choose its method from a panel of rigid, pre-defined methods, hereas selecting a path within a method consists o choosing the appropriate path for the situation at hand. Finally, selecting and tuning a m thod allows each project to select meth ds from different [9] approaches and tune them to the p oject's needs.”

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Quality of methods[edit] As the quality of process models is being discussed in this paper, there is a need to elaborate quality of modeling techniques as an important essence in quality of process models. In most existing framework created for understanding the quality, the line between quality of modeling techniques and the quality of models as a result of the application of those techniques are not clearly drawn. This report will concentrate both on quality of process modeling techniques and quality of process models to clearly differentiate the two. Various frameworks were developed to help in understanding quality of process modeling techniques, one example is Quality based modeling evaluation framework or known as QMe framework which argued to provide set of well defined quality properties and procedures to make an objective assessment of this properties possible.[10] This framework also has advantages of providing uniform and formal description of the model element within one or different model types using one modeling techniques [10] In short this can make assessment of both the product quality and the process quality of modeling techniques with regard to a set of properties that have been defined before. Quality properties that relate to business process modeling techniques discussed in [10] are: Expressiveness: the degree to which a given modeling technique is able to denote the models of any number and kinds of application domains. Arbitrariness: the degree of freedom one has when modeling one and the same domain Suitability: the degree to which a given modeling technique is specifically tailored for a specific kind of application domain. Comprehensibility: the ease with which the way of working and way of modeling are understood by participants. Coherence: the degree to which the individual sub models of a way of modeling constitute a whole. Completeness; the degree to which all necessary concepts of the application domain are represented in the way of modeling. Efficiency: the degree to which the modeling process uses resources such as time and people. Effectiveness: the degree to which the modeling process achieves its goal. To assess the quality of Q-ME framework; it is used to illustrate the quality of the dynamic essentials modeling of the organisation (DEMO) business modeling techniques. It is stated that the evaluation of the Q-ME framework to the DEMO modeling techniques has revealed the shortcomings of Q-ME. One particular is that it does not include quantifiable metric to express the quality of business modeling technique which makes it hard to compare quality of different techniques in an overall rating. There is also a systematic approach for quality measurement of modeling techniques known as complexity metrics suggested by Rossi et al. (1996). Techniques of Meta model is used as a basis for computation of these complexity metrics. In comparison to quality framework 

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proposed by Krogstie, quality measurement focus more on technical level instead of individual model level.[11] Authors (Cardoso, Mendling, Neuman and Reijers, 2006) used complexity metrics to measure the simplicity and understandability of a design. This is supported by later research done by Mendling et al. who argued that without using the quality metrics to help question quality properties of a model, simple process can be modeled in a complex and unsuitable way. This in turn can lead to a lower understandability, higher maintenance cost and perhaps inefficient execution of the process in question.[12] The quality of modeling technique is important in creating models that are of quality and contribute to the correctness and usefulness of models. Quality of models[edit] Earliest process models reflected the dynamics of the process with a practical process obtained by instantiation in terms of relevant concepts, available technologies, specific implementation environments, process constraints and so on. [13] Enormous number of research has been done on quality of models but less focus has been shifted towards the quality of process models. Quality issues of process models cannot be evaluated exhaustively however there are four main guidelines and frameworks in practice for such. These are: top-down quality frameworks, bottom-up metrics related to quality [14] aspects, empirical surveys related to modeling techniques, and pragmatic guidelines. Hommes quoted Wang et al. (1994)[11] that all the main characteristic of quality of models can all be grouped under 2 groups namely correctness and usefulness of a model, correctness ranges from the model correspondence to the phenomenon that is modeled to its correspondence to syntactical rules of the modeling and also it is independent of the purpose to which the model is used. Whereas the usefulness can be seen as the model being helpful for the specific purpose at hand for which the model is constructed at first place. Hommes also makes a further distinction between internal correctness (empirical, syntactical and semantic quality) and external correctness (validity). A common starting point for defining the quality of conceptual model is to look at the linguistic properties of the modeling language of which syntax and semantics are most often applied. Also the broader approach is to be based on semiotics rather than linguistic as was done by Krogstie using the top-down quality framework known as SEQUAL. [15][16] It defines several quality aspects based on relationships between a model, knowledge Externalisation, domain, a modeling language, and the activities of learning, taking action, and modeling. The framework does not however provide ways to determine various degrees of quality but has been used extensively for business process modeling in empirical tests carried out[17] According to previous research done by Moody et al.[18] with use of conceptual model

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quality framework proposed by Lindland et al. (1994) to evaluate quality of process model, three levels of quality[19] were identified: Syntactic quality: Assesses extent to which the model conforms to the grammar rules of modeling language being used. Semantic quality: whether the model accurately represents user requirements Pragmatic quality: whether the model can be understood sufficiently by all relevant stakeholders in the modeling process. That is the model should enable its interpreters to make use of it for fulfilling their need. From the research it was noticed that the quality framework was found to be both easy to use and useful in evaluating the quality of process models however it had limitations in regards to reliability and difficult to identify defects. These limitations led to refinement of the framework through subsequent research done by Krogstie. This framework is called SEQUEL framework by Krogstie et al. 1995 (Refined further by Krogstie&Jørgensen, 2002) which included three more quality aspects. Physical quality: whether the externalized model is persistent and available for the audience to make sense of it. Empirical quality: whether the model is modeled according to the established regulations regarding a given language. Social quality: This regards the agreement between the stakeholders in the modeling domain. Dimensions of Conceptual Quality framework [20] Modeling Domain is the set of all statements that are relevant and correct for describing a problem domain, Language Extension is the set of all statements that are possible given the grammar and vocabulary of the modeling languages used. Model Externalization is the conceptual representation of the problem domain. It is defined as the set of statements about the problem domain that are actually made. Social Actor Interpretation and Technical Actor Interpretation are the sets of statements that actors both human model users and the tools that interact with the model, respectively ‘think’ the conceptual representation of the problem domain contains. Finally, Participant Knowledge is the set of statements that human actors, who are involved in the modeling process, believe should be made to represent the problem domain. These quality dimensions were later divided into two groups that deal with physical and social aspects of the model. In later work, Krogstie et al.[15] stated that while the extension of the SEQUAL framework has fixed some of the limitation of the initial framework, however other limitation remain . In particular, the framework is too static in its view upon semantic quality, mainly considering models, not modeling activities, and comparing these models to a static domain rather than seeing the model as a facilitator for changing the domain. 

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Also, the framework’s definition of pragmatic quality is quite narrow, focusing on understanding, in line with the semiotics of Morris, while newer research in linguistics and semiotics has focused beyond mere understanding, on how the model is used and impact its interpreters. The need for a more dynamic view in the semiotic quality framework is particularly evident when considering process models, which themselves often prescribe or even enact actions in the problem domain, hence a change to the model may also change the problem domain directly. This paper discusses the quality framework in relation to active process models and suggests a revised framework based on this. Further work by Krogstie et al. (2006) to revise SEQUAL framework to be more appropriate for active process models by redefining physical quality with a more narrow interpretation than previous research.[15] The other framework in use is Guidelines of Modeling (GoM) [21] based on general accounting principles include the six principles: Correctness, Clarity deals with the comprehensibility and explicitness (System description) of model systems. Comprehensibility relates to graphical arrangement of the information objects and, therefore, supports the understand ability of a model. Relevance relates to the model and the situation being presented. Comparability involves the ability to compare models that is semantic comparison between two models, Economic efficiency; the produced cost of the design process need at least to be covered by the proposed use of cost cuttings and revenue increases. Since the purpose of organizations in most cases is the maximization of profit, the principle defines the borderline for the modeling process. The last principle is Systematic design defines that there should be an accepted differentiation between diverse views within modeling. Correctness, relevance and economic efficiency are prerequisites in the quality of models and must be fulfilled while the remaining guidelines are optional but necessary. The two frameworks SEQUAL and GOM have a limitation of use in that they cannot be used by people who are not competent with modeling. They provide major quality metrics but are not easily applicable by non-experts. The use of bottom-up metrics related to quality aspects of process models is trying to bridge the gap of use of the other two frameworks by non-experts in modeling but it is mostly theoretical and no empirical tests have been carried out to support their use. Most experiments carried out relate to the relationship between metrics and quality aspects and these works have been done individually by different authors: Canfora et al. study the connection mainly between count metrics (for example, the number of tasks or splits -and maintainability of software process models;[22] Cardoso validates the correlation between control flow complexity and perceived complexity; and Mendling et al. use metrics to predict control flow errors such as deadlocks in process models.[12][23]

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The results reveal that an increase in size of a model appears to have a negative impact on quality and their comprehensibility. Further work by Mendling et al. investigates the connection between metrics and understanding [24] and[25] While some metrics are confirmed regarding their impact, also personal factors of the modeler – like competence – are revealed as important for understanding about the models. Several empirical surveys carried out still do not give clear guidelines or ways of evaluating the quality of process models but it is necessary to have clear set of guidelines to guide modelers in this task. Pragmatic guidelines have been proposed by different practitioners even though it is difficult to provide an exhaustive account of such guidelines from practice. In,[26] 10 tips for process modeling are summarized, many technical definitions and rules are provided, but it does not teach how to create process models that are effective in their primary mission - maximizing shared understanding of the as-is or to-be process. Most of the guidelines are not easily put to practice but “label activities verb–noun” rule has been suggested by other practitioners before and analyzed empirically. From the research.[27] value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels which need to be analyzed. It was found that it results in better models in terms of understanding than alternative labelling styles. From the earlier research and ways to evaluate process model quality it has been seen that the process model's size, structure, expertise of the modeler and modularity have an impact on its overall understandability. [24] [28] Based on these a set of guidelines was presented [29] 7 Process Modeling Guidelines (7PMG). This guideline uses the verb-object style, as well as guidelines on the number of elements in a model, the application of structured modeling, and the decomposition of a process model. The guidelines are as follows: G1 Minimize the number of elements in a model G2 Minimize the routing paths per element G3 Use one start and one end event G4 Model as structured as possible G5 Avoid OR routing elements G6 Use verb-object activity labels G7 Decompose a model with more than 50 elements 7PMG still though has limitations with its use: Validity problem 7PMG does not relate to the content of a process model, but only to the way this content is organized and represented. It does suggest ways of organizing different structures of the process model while the content is kept intact but the pragmatic issue of what must be included in the model is still left out. The second limitation relates to the prioritizing guideline the derived ranking has a small empirical basis as it relies on the involvement of 21 process modelers only. This could be seen on the one hand as a need for a wider involvement of process modelers’ experience, but it also rises the question what alternative approaches may be available to arrive at a prioritizing guideline.[29] 

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5.5 Process Definition Techniques Software Engineering Process Concepts 3.1.1 Themes Dowson [35] notes that “All process work is ultimately directed at ‘software process assessment and improvement’”. This means that the objective is to implement new or better processes in actual practices, be they individual, project or organizational practices.

We describe the main topics in the software process engineering (i.e., the meta-level that has been alluded to earlier) area in terms of a cycle of process change, based on the commonly known PDCA cycle. This cycle highlights that individual process engineering topics are part of a larger process to improve practice, and that process evaluation and feedback is an important element of process engineering. Software process engineering consists of four activities as illustrated in the model in Figure 1. The activities are sequenced in an iterative cycle allowing for continuous feedback and improvement of the software process. The “Establish Process Infrastructure” activity consists of establishing commitment to process implementation and change (including obtaining management buy-in), and putting in place an appropriate infrastructure (resources and responsibilities) to make it happen. The activities “Planning of Process Implementation and Change” and “Process Implementation and Change” are the core ones in process engineering, in that they are essential for any long-lasting benefit from process engineering 129

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to accrue. In the planning activity the objective is to understand the current business objectives and process needs of the organization1, identify its strengths and weaknesses, and make a plan for process implementation and change. In “Process Implementation and Change”, the objective is to execute the plan, deploy new processes (which may involve, for example, the deployment of tools and training of staff), and/or change existing processes. The fourth activity, “Process Evaluation” is concerned with finding out how well the implementation and change went; whether the expected benefits materialized. This is then used as input for subsequent cycles. At the centre of the cycle is the “Process Experience Base”. This is intended to capture lessons from past iterations of the cycle (e.g., previous evaluations, process definitions, and plans). Evaluation lessons can be qualitative or quantitative. No assumptions are made about the nature or technology of this “Process Experience Base”, only that it be a persistent storage. It is expected that during subsequent iterations of the cycle, previous experiences will be adapted and reused. It is also important to continuously re-assess the utility of information in the experience base to ensure that obsolete information does not accumulate. With this cycle as a framework, it is possible to map the topics in this knowledge area to the specific activities where they would be most relevant. This mapping is also shown in Figure 1. The bulleted boxes contain the Knowledge Area topics. It should be noted that this cycle is not intended to imply that software process engineering is relevant to only large organizations. To the contrary, process-related activities can, and have been, performed successfully by small organizations, teams, and individuals. The way the activities defined in the cycle are performed would be different depending on the context. Where it is relevant, we will present examples of approaches for small organizations.

Figure 1 A model of the software process engineering cycle, and the relationship of its activities to the KA topics. 130

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The circles are the activities in the process engineering cycle. The square in the middle of the cycle is a data store. The bulleted boxes are the topics in this Knowledge Area that map to each of the activities in the cycle. The numbers refer to the topic sections in this chapter. The topics in this KA are as follows: Process Infrastructure: This is concerned with putting in place an infrastructure for software process engineering. Process Measurement: This is concerned with quantitative techniques to diagnose software processes; to identify strengths and weaknesses. This can be performed to initiate process implementation and change, and afterwards to evaluate the consequences of process implementation and change. Process Definition: This is concerned with defining processes in the form of models, plus the automated support that is available for the modeling task, and for enacting the models during the software process. Qualitative Process Analysis: This is concerned with qualitative techniques to analyze software processes, to identify strengths and weaknesses. This can be performed to initiate process implementation and change, and afterwards to evaluate the consequences of process implementation and change. Process Implementation and Change: This is concerned with deploying processes for the first time and with changing existing process. This topic focuses on organizational change. It describes the paradigms, infrastructure, and critical success factors necessary for successful process implementation and change. Within the scope of this topic, we also present some conceptual issues about the evaluation of process change. The main, generally accepted, themes in the software engineering process field have been described by Dowson in [35]. His themes are a subset of the topics that we cover in this KA. Below are Dowson’s themes: Process definition: covered in topic 3.4 of this KA breakdown Process assessment: covered in topic 3.3 of this KA breakdown Process improvement: covered in topics 3.2 and 3.6 of this KA breakdown Process support: covered in topic 3.4 of this KA breakdown We also add one theme in this KA description, namely the qualitative process analysis (covered in topic 3.5). 3.1.2 Terminology There is no single universal source of terminology for the software engineering process field, but good sources that define important terms are [51][96], and the vocabulary (Part 9) in the ISO/IEC TR 15504 documents [81]. 3.2 Process Infrastructure

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At the initiation of process engineering, it is necessary to have an appropriate infrastructure in place. This includes having the resources (competent staff, tools and funding), as well as the assignment of responsibilities. This is an indication of management commitment to and ownership of the process engineering effort. Various committees may have to be established, such as a steering committee to oversee the process engineering effort. It is widely recognized that a team separate from the developers/maintainers must be set up and tasked with process analysis, implementation and change [16]. The main reason for this is that the priority of the developers/maintainers is to produce systems or releases, and therefore process engineering activities will not receive as much attention as they deserve or need. This, however, should not mean that the project organization is not involved in the process engineering effort at all. To the contrary, their involvement is essential. Especially in a small organization, outside help (e.g., consultants) may be required to assist in making up a process team. Two types of infrastructure are have been used in practice: the Experience Factory [8][9] and the Software Engineering Process Group [54]. The IDEAL handbook [100] provides a good description of infrastructure for process improvement in general. 3.2.1 The Software Engineering Process Group The SEPG is intended to be the central focus for process improvement within an organization. The SEPG typically has the following ongoing activities: Obtains and maintains the support of all levels of management Facilitates software process assessments (see below) Works with line managers whose projects are affected by changes in software engineering practice Maintains collaborative working relationships with software engineers Arranges and supports any training or continuing education related to process implementation and Change Tracks, monitors, and reports on the status of particular improvement efforts Facilitates the creation and maintenance of process definitions Maintains a process database Provides process consultation to development projects and management Participate in integrating software engineering processes with other organizational processes, such as systems engineering Fowler and Rifkin [54] suggest the establishment of a steering committee consisting of line and supervisory management. This would allow management to guide process implementation and change, align this effort with 132

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strategic and business goals of the organization, and also provides them with visibility. Furthermore, technical working groups may be established to focus on specific issues, such as selecting a new design method to setting up a measurement program. 3.2.2 The Experience Factory The concept of the EF separates the project organization (e.g., the software development organization) from the improvement organization. The project organization focuses on the development and maintenance of applications. The EF is concerned with improvement. Their relationship is depicted in Figure 2. The EF is intended to institutionalize the collective learning of an organization by developing, updating, and delivering to the project organization experience packages (e.g., guide books, models, and training courses).2 The project organization offers to the experience factory their products, the plans used in their development, and the data gathered during development and operation. Examples of experience packages include: resource models and baselines3 (e.g., local cost models, resource allocation models) change and defect baselines and models (e.g., defect prediction models, types of defects expected for the application)  project models and baselines (e.g., actual vs. expected product size)  process definitions and models (e.g., process models for Cleanroom, Ada waterfall model) method and technique evaluations (e.g., best method for finding interface faults)  products and product parts (e.g., Ada generics for simulation of satellite orbits) quality models (e.g., reliability models, defect slippage models, ease of change models), and lessons learned (e.g., risks associated with Ada development).

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Figure 2 The relationship between the Experience Factoryand the project organization as implemented at the Software Engineering Laboratory at NASA/GSFC. Thisdiagram is reused here from [10] with permission of the authors. 3.3 Process Measurement Process measurement, as used here, means that quantitativeinformation about the process is collected, analyzed, and interpreted. Measurement is used to identify the strengthsand weaknesses of processes, and to evaluate processes after they have been implemented and/or changed (e.g.,evaluate the ROI from implementing a new process).4 An important assumption made in most process engineering work is illustrated by the path diagram in Figure 3. Here, we assume that the process has an impact on process outcomes. Process outcomes could be, for example, product quality (faults per KLOC or per FP), maintainability (effort to make a certain type of change), productivity (LOC or FP per person month), time-to-market, the extent of process variation, or customer satisfaction (as measured through a customer survey). This relationship depends on the particular context (e.g., size of the organization, or size of the project).

Not every process will have a positive impact on all outcomes. For example, the introduction of software inspections may reduce testing effort and cost, but may increase interval time if each inspection introduces large delays due to the scheduling of large inspection meetings [131]. Therefore, it is preferred to use multiple process outcome measures that are important for the organization’s business. In general, we are most concerned about the process outcomes. However, in order to achieve the process outcomes that we desire (e.g., better quality, better maintainability, greater customer satisfaction) we have to implement the appropriate process. Of course, it is not only process that has an impact on outcomes. Other factors such as the capability of the staff and the tools that are used play an important role.5 Furthermore, the extent to which the process is institutionalized or implemented (i.e., process fidelity) is important as it may explain why “good” processes do not give the desired outcomes. One can measure the quality of the software process itself, or the process outcomes. The methodology in Section 3.3.1 is applicable to both. We will focus in Section 3.3.2 on process measurement since the measurement of process outcomes is more general and applicable in other Knowledge Areas.

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3.3.1 Methodology in Process Measurement A number of guides for measurement are available [108][109][126]. All of these describe a goal-oriented process for defining measures. This means that one should start from specific information needs and then identify the measures that will satisfy these needs, rather than start from specific measures and try to use them. A good practical text on establishing and operating a measurement program has been produced by the Software Engineering Laboratory [123]. This also discusses the cost of measurement. Texts that present experiences in implementing measurement in software organizations include [86][105][115]. An emerging international standard that defines a generic measurement process is also available (ISO/IEC CD 15939: Information Technology – Software Measurement Process) [82]. Two important issues in the measurement of software engineering processes are the reliability and validity of measurement. Reliability is concerned with random measurement error. Validity is concerned with the ability of the measure to really measure what we think it is measuring. Reliability becomes important when there is subjective measurement, for example, when assessors assign scores to a particular process. There are different types of validity that ought to be demonstrated for a software process measure, but the most critical one is predictive validity. This is concerned with the relationship between the process measure and the process outcome. A discussion of both of these and different methods for achieving them can be found in [40][59]. An IEEE Standard describes a methodology for validating metrics ( IEEE Standard for a Software Quality Metrics Methodology. IEEE Std 10611998) [76]. An overview of existing evidence on reliability of software process assessments can be found in [43][49], and for predictive validity in [44][49][59][88]. 3.3.2 Process Measurement Paradigms Two general paradigms that are useful for characterizing the type of process measurement that can be performed have been described by Card [21]. The distinction made by Card is a useful conceptual one. Although, there may be overlaps in practice. The first is the analytic paradigm. This is characterized as relying on “quantitative evidence to determine where improvements are needed and whether an 135

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improvement initiative has been successful”.6 The second, the benchmarking paradigm, “depends on identifying an ‘excellent’ organization in a field and documenting its practices and tools”. Benchmarking assumes that if a lessproficient organization adopts the practices of the excellent organization, it will also become excellent. Of course, both paradigms can be followed at the same time, since they are based on different types of information. We use these paradigms as general titles to distinguish between different types of measurement. 3.3.2.1 Analytic Paradigm7 The analytic paradigm is exemplified by the Quality Improvement Paradigm (QIP) consisting of a cycle of understanding, assessing, and packaging [124]. Experimental and Observational Studies Experimentation involves setting up controlled or quasi experiments in the organiza tion to evaluate processes [101]. Usually, one would compare a new process with the current process to determine whether the former has better process outcomes. Correlational (nonexperimental) studies can also provide useful feedback for identifying process improvements (e.g., for example, see the study described by Agresti [2]). Process Simulation The process simulation approach can be used to predict process outcomes if the current process is changed in a certain way [117]. Initial data about the performance of the current process needs to be collected, however, as a basis for the simulation. Orthogonal Defect Classification Orthogonal Defect Classification is a technique that can be used to link faults found with potential causes. It relies on a mapping between fault types and fault triggers [22][23]. There exists an IEEE Standard on the classification of faults (or anomalies) that may also be useful in this context ( IEEE Standard for the Classification of Software Anomalies. IEEE Std 1044-1993) [74]. Statistical Process Control Placing the software process under statistical process control, through the use of control charts and their interpretations, is an effective way to identify stability, or otherwise, in the process. One recent book provides a good introduction to SPC in the context of software engineering [53]. The Personal Software Process This defines a series of improvements to an individual’s development practices in a specified order [70]. It is ‘bottom-up’ in the sense that it stipulates personal data collection and improvements based on the data terpretations. 3.3.2.2 Benchmarking Paradigm

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This paradigm involves measuring the maturity of an organization or the capability of its processes. The benchmarking paradigm is exemplified by the softwa re process assessment8 work. A general introductory overview of process assessments and their application is provided in [135]. Process assessment models An assessment model captures what are believed to be good practices. The good practices may pertain to technical software engineering activities only, or may also encompass, for example, management, systems engineering, and human resources management activities as well. Architectures of assessment models There are two general architectures for an assessment model that make different assumptions about the order in which processes must be measured: the continuous and the staged architectures [110]. At this point it is not possible to make a recommendation as to which approach is better than another. They have considerable differences. An organization should evaluate them to see which are most pertinent to their needs and objectives when selecting a model. Assessment models The most commonly used assessment model in the software community is the SW-CMM [122]. It is also important to recognize that ISO/IEC 15504 is an emerging international standard on software process assessments [42][81]. It defines an exemplar assessment model and conformance requirements on other assessment models. ISO 9001 is also a common model that has been applied by software organizations (usually in conjunction with ISO 9000-1) [132]. Other notable examples of assessment models are Trillium [25], Bootstrap [129], and the requirements engineering capability model [128]. There are also maturity models for other software processes available, such as for testing [18][19][20], a measurement maturity model [17], and a maintenance maturity model [36] (although, there have been many more capability and maturity models that have been defined, for example, for design, documentation, and formal methods, to name a few). A maturity model for systems engineering has also been developed, which would be useful where a project or organization is involved in the development and maintenance of systems including software [39]. The applicability of assessment models to small organizations is addressed in [85][120], where assessments models tailored to small organizations are presented. 137

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Process assessment methods In order to perform an assessment, a specificassessment method needs to be followed. In additionto producing a quantitative score that characterizes thecapability of the process (or maturity of theorganization), an important purpose of an assessmentis to create a climate for change within theorganization [37]. In fact, it has been argued that thelatter is the most important purpose of doing anassessment [38].The most well known method that has a reasonableamount of publicly available documentation is theCBA IPI [37]. This method focuses on assessments for the purpose of process improvement using theSW-CMM. Many other methods are refinements ofthis for particular contexts. Another well knownmethod using the SW-CMM, but for supplierselection, is the SCE [6]. The activities performedduring an assessment, the distribution of effort onthese activities, as well as the atmosphere during anassessment is different if it is for the purpose ofimprovement versus contract award. Requirements on both types of methods that reflect what are believed tobe good assessment practices are provided in [81][99]. There have been criticisms of various models and methodsfollowing the benchmarking paradigm, for example [12][50][62][87]. Most of these criticisms were concernedwith the empirical evidence supporting the use of assessments models and methods. However, since thepublication of these articles, there has been anaccumulation of systematic evidence supporting theefficacy of process assessments [24][47][48][60][64][65][66][94]. 3.4 Process Definition Software engineering processes are defined for a number ofreasons, including: facilitating human understanding andcommunication, supporting process improvement,supporting process management, providing automated process guidance, and providing automated executionsupport [29][52][68]. The types of process definitions required will depend, at least partially, on the reason.It should be noted also that the context of the project and organization will determine the type of process definitionthat is most important. Important variables to consider include the nature of the work (e.g., maintenance ordevelopment), the application domain, the structure of the delivery process (e.g., waterfall, incremental, evolutionary),and the maturity of the organization.There are different approaches that can be used to defineand document the process. Under this topic the approachesthat have been presented in the literature are covered,although at this time there is no data on the extent to which 138

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these are used in practice. Types of Process Process Definitions Definitions Processes can be defined at different levels ofabstraction (e.g., generic definitions vs. tailoreddefinitions, descriptive vs. prescriptive vs.proscriptive). The differentiation amongst these hasbeen described in [69][97][111]. Orthogonal to the levels above, there are also types ofprocess definitions. For example, a process definition can be a procedure, a policy, or a standard. 3.4.2 Life Cycle Framework Models These framework models serve as a high leveldefinition of the phases that occur duringdevelopment. They are not detailed definitions, butonly the high level activities and theirinterrelationships. The common ones are: the waterfallmodel, throwaway prototyping model, evolutionaryprototyping model, incremental/iterative development, spiral model, reusable software model, and automatedsoftware synthesis. (see [11][28][84][111][113]). Comparisons of these models are provided in [28][32], and a method for selection amongst many ofthem in [3]. 3.4.3 Software Life Cycle Process Models Definitions of life cycle process models tend to bemore detailed than framework models. Anotherdifference being that life cycle process models do notattempt to order their processes in time. Therefore, inprinciple, the life cycle processes can be arranged tofit any of the life cycle frameworks. The two mainreferences in this area are ISO/IEC 12207: Information Information Technology – Software Life CycleProcesses CycleProcesses [80] and ISO/IEC TR 15504: Information Technology Technology – Software Software Process Process Assessme Assessment nt [42][81].Extensive guidance material for the application of the former has been produced by the IEEE (Guide forInfor forInformatio mation n Technology Technology - Software Software Life Cycle Processes Processes - Life cycle cycle data data, IEEE Std 12207.1-1998,and Guide for Information Technology Software Life Cycle Cycle Proces Processes ses – Imple Implemen mentat tatio ion. n. Consid Consider erati ations ons.IEEE Std 12207.2-1998) [77][78]. The latter defines a two dimensional model with one dimension beingprocesses, and the second a measurement scale to evaluate the capability of the processes. In principle,ISO/IEC 12207 would serve as the process dimension of ISO/IEC 15504.The IEEE standard on developing life cycle processesalso provides a list of processes and activities fordevelopment and maintenance ( IEEE Standard forDeveloping Software Life Cycle Processes, IEEE Std1074-1991) [73], and provides examples of mappingthem to life cycle framework models. A standard that 139

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focuses on maintenance processes is also availablefrom the IEEE ( IEEE Standard for Software Maintenance, IEEE Std 1219-1992) [75]. 3.4.4 Notations for Process Definitions Different elements of a process can be defined, forexample, activities, products (artifacts), and resources [68]. Detailed frameworks that structure the types of informationrequired to define processes are described in [4][98]. There are a large number of notations that have been usedto define processes. They differ in the types of informationdefined in the above frameworks that they capture. A textthat describes different notations is [125]. Because there is no data on which of these was found to bemost bemost useful or easiest to use under which conditions, this Guide covers what seemingly are popular approaches inpractice: data flow diagrams [55], in terms of process purpose and outcomes [81], as a list of processesdecomposed in constituent activities and tasks tasks define defined d in natural language [80], Statecharts [89][117] (also see [63]for a comprehensive description of Statecharts), ETVX [116], Actor-Dependency modeling [14][134], SADTnotation [102], Petri nets [5], IDEF0 [125], rule-based [7], and System Dynamics [1]. Other process programminglanguages programminglanguages have been devised, and these are described in [29][52][68]. 3.4.5 Process Definition Methods These methods specify the activities that must beperformed in order to develop and maintain a processdefinition. These may include eliciting information fromdevelopers to build a descriptive process definition fromscratch, and to tailoring an existing standard or commercialprocess. Examples of methods that have been applied inpractice are [13][14][90][98][102]. In general, there is astrong similarity amongst them in that they tend to follow atraditional software development life cycle. 3.4.6 Automation Automated tools either support the execution of the processdefinitions, or they provide guidance to humans performingthe defined processes. In cases where process analysis isperformed, some tools allow different types of simulations(e.g., discrete event simulation).There exist tools that support each of the above processdefinition notations. Furthermore, these tools can executethe process definitions to provide automated support to theactual processes, or to fully automate them in someinstances. An overview of process modeling tools can befound in [52], and of process-centered environments in[57][58].Recent 140

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work on the application of the Internet to theprovision of real-time process guidance is described in [91]. 3.5 Qualitative Process Analysis The objective of qualitative process analysis is to identifythe strengths and weaknesses of the software process. It canbe performed as a diagnosis before implementing orchanging a process. It could also be performed after a process is implemented or changed to determine whetherthe change has had the desired effect.Below we present two techniques for qualitative analysisthat have been used in practice. Although it is plausible that new techniques would emerge in the future. 3.5.1 Process Definition Review Qualitative evaluation means reviewing a process definition(either a descriptive or a prescriptive one, or both), and identifying deficiencies and potential processimprovements. Typical examples of this are presented in[5][89]. An easily operational way to analyze a process is tocompare it to an existing standard (national, international, or professional body), such as ISO/IEC 12207 [80].With this approach, one does not collect quantitative data on the process. Or if quantitative data is collected, it plays asupportive role. The individuals performing the analysis ofthe process definition use their knowledge and capabilitiesto decide what process changes changes would potentially lead todesirable process outcomes. outcomes. 3.5.2 Root Cause Analysis A nalysis Another common qualitative technique that is used inpractice is a “Root Cause Analysis”. This involves tracing back from detected problems (e.g., faults) to identify theprocess causes, with the aim of changing the process to avoid the problems in the future. Examples of this fordifferent types of processes are described in[13][27][41][107]. With this approach, one starts from the process outcomes,and traces back along the path in Figure 3 to identify the process causes of the undesirable outcomes. TheOrthogonal Defect Classification technique described in Section 3.3.2.1 can be considered a more formalizedapproach to root cause analysis using quantitative information. 3.6 Process Implementatio Implementation n and Change This topic describes the situation when processes aredeployed for the first time (e.g., introducing an inspection

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process within a project or a complete methodology, suchas Fusion [26] or the Unified Process [83]), and when current processes are changed (e.g., introducing a tool, oroptimizing a procedure).9 In both instances, existing practices have to be modified. If the modifications areextensive, then changes in the organizational culture may be necessary. 3.6.1 Paradigms for Process Implementation and Change Two general paradigms that have emerged for drivingprocess implementation and change are the Quality Improvement Paradigm (QIP) [124] and the IDEAL model

5.6 Etvx ( Entry Task Validation Exit) (E)ntry Criteria Business Requirements available for review  (T)asks Review Business Requirements  Log all discrepancies/ questions in Bug Tracker  Provide High-level Test Estimates  (V)alidation All items in the bug tracker are closed with valid comments  Business Requirements document is updated  (E)xit Signed-off Business Requirement Document  High-level Test estimates are accepted by all stake holders  Design Phase (E)ntry Criteria Signed-off Business Requirements  Functional (FS) & Technical(TS) Specifications available for review  (T)asks Review FS & TS  Raise questions/discrepancies in Bug Tracker to clarify if anything is ambiguous  Provide detailed Test Estimates  Review project plan and provide feedback to Program Manager  Create master test plan & automation test plan and get it reviewed by appropriate  stakeholders Generate traceability matrix  Collaborate with all test teams to have complete coverage on integration areas  142

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(V)alidation All items in the bug tracker are in closed state  All review comments of master test plan and automation test plan are closed  (E)xit Signed-off TS  Signed-off FS  Detailed Test Estimates are accepted by all stake holders and incorporated appropriately  in schedule Signed-off project plan  Signed-off MTP  Signed-off ATP  Build/Coding Phase (E)ntry Criteria Signed-off TS  Signed-off FS  Signed-off MTP  Signed-off ATP  Availability of Test Environment details  (T)asks Write test cases to cover entire functionality and affected areas both from UI and DB  perspective Get the test cases reviewed by appropriate stakeholders and get sign off  Work with Operations /Support team to get test environments  Validate SQL scripts against test cases in Dev/Unit Test environments  (V)alidation Test cases review  Sanity check of test environments  Sanity check of SQL scripts and UI automation scripts  (E)xit Signed-off test cases  SQA environments are available for build deployment  Validated SQL scripts against test cases  Validated UI automation scripts  Stabilization/Testing Phase (E)ntry Criteria Signed-off test cases  Validated UI automation scripts  Validated SQL scripts against test cases  SQA environments are available for build deployment  143

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(T)asks Execute test cases both manual and automation  Publish daily report with test cases execution progress as well updated info on bugs  Raise bugs in Bug Tracker appropriately  Track bugs to closure  Collaborate with all test teams to have complete coverage on integration areas  (V)alidation Execution of test cases is completed  All appropriate bugs are tracked to closure  (E)xit All the BVTs passed on all the builds/patches  Code Complete Build:  Test should be able to execute 100% of test cases by end of code freeze build and 80%  of test cases should pass By start of Test final build :  All failed test cases of Code complete build + other test cases planned should pass.  All bugs raised during the Code complete build execution should be resolved &  closed  No S1 & S2 bugs should be in proposed/active/resolved state Active S3 & S4 bugs count should be within permissible limit like 5% of total bugs  or moved to future releases Any known issues with technical constraints or anything should have an agreed  resolution Test Final Build:  All planned test cases for final build should pass   No S1 & S2 bugs should be in proposed/active/resolved state  No S3 & S4 bugs should be in proposed/active/resolved state

5.7 Process Baselining There are many different steps that organizations follow in benchmarking. However, most baselining processes have these four steps: 1. Develop a clearly defined baseline in your organization: This means that all of the attributes involved in your baseline are defined. In our example of defects per lines of code, clearly defining what is meant by defect and a line of code would meet the objective of this step. 2. Identify the organizations you desire to baseline against: Many factors come into this decision, such as do you want to benchmark within your industry, do you want to benchmark what you believe are leading organizations, do you want to benchmark an organization that 144

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uses the same tools that are used in your organization, and do you want to benchmark against organizations with a similar culture. 3. Compare baseline calculations: Compare how your baseline is calculated versus the baseline calculation in the company you want to benchmark against. Benchmarking is only effective when you benchmark against an organization who has calculated their baseline using approximately the same approach that your organization used to calculate the baseline. 4. Identify the cause of baseline variance in the organization you benchmarked against: When you find a variance between the baseline calculation in your company and the baseline calculation in the organization you are benchmarking against, you need to identify the cause of variance. For example, if your organization was producing 20 defects per thousand lines of code, and you benchmarked against an organization that only had 10 defects per thousand lines of code you would want to identify the cause of the difference. If you cannot identify the cause of difference, there is little value in benchmarking. Let us assume that the company you benchmarked against had a different process for requirement definition than your organization. For example, assume they use JAD (joint application development) and you did not. Learning this, you may choose to adopt JAD in your organization as a means for reducing your developmental defect rates. 5.8 Process Assessment and Improvement Software process improvement (SPI) started in the 1990s from the process based approach to software development. The main problem of product-centred development was the ignoring of activities that had no visible results and regarding them as unimportant. Process-based approach in software development puts emphasis on organisation development and the reaching of business goals. A similar understanding of software processes creates a feeling of unity among the developers in a company and a continuity in the development, that in turn guarantee higher production capability and quality of the results.

As the first promoter of software process improvement Watts Humphrey said, the main problems in software development are not caused by insufficient skills, but by unawareness of how to use the best available methods and inability to efficiently solve detailed problems related to the process and product. The result of software process improvement will be the following of a detailed description of activities in every situation of development. Software process improvement begins with an assessment of software processes. Different process models and standards have been created for software process assessment. We employ the two most widely used models CMMI or Capability Maturity Model Integration and ISO/IEC 15504 (SPICE – Software Process Improvement and Capability Determination). The processes related to development are assessed on the basis of a benchmark model, i.e. each specific process to be assessed in relation to the development 145

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will be compared to the requirements described in the benchmark model, at the same time taking into account also the unique character of the development company or a project. Process assessment is project-based – a recently ended project is chosen and the activities related to its development will be evaluated. Assessment starts with a meeting for developers that introduces software process improvement and assessment and in the course of which the project as well as the processes are chosen that are considered by developers to be the most important for assessment. Often these tend to be the processes that the developers consider to have been insufficient in several projects. The detailed assessment of the chosen project and processes will be done during an interview with the developers who took part in the project. Software process assessment also ends with a development team meeting, where the capabilities of the assessed processes are described according to process models, as well as the shortcomings of the processes and a process improvement plan is put together. Despite the fact that the processes are assessed in a specific project, the development processes will be improved in each of the following development projects. It is only constant improvement that leads to the software productivity growth in a company. 5.9 CMMI A maturity level is a well-defined evolutionary plateau toward achieving a mature software process. Each maturity level provides a layer in the foundation for continuous process improvement. In CMMI models with a staged representation, there are five maturity levels designated by the numbers 1 through 5 1. Initial 2. Managed 3. Defined 4. Quantitatively Managed 5. Optimizing CMMI Staged Represenation- Maturity Levels

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Now we will give more detail about each maturity level. Next section will list down all the process areas related to these maturity levels. Maturity Level Details: Maturity levels consist of a predefined set of process areas. The maturity levels are measured by the achievement of the specific and generic goals that apply to each predefined set of process areas. The following sections describe the characteristics of each maturity level in detail. Maturity Level 1 - Initial At maturity level 1, processes are usually ad hoc and chaotic. The organization usually does not provide a stable environment. Success in these organizations depends on the competence and heroics of the people in the organization and not on the use of proven processes. Maturity level 1 organizations often produce products and services that work; however, they frequently exceed the budget and schedule of their projects. Maturity level 1 organizations are characterized by a tendency to over commit, abandon processes in the time of crisis, and not be able to repeat their past successes. Maturity Level 2 - Managed At maturity level 2, an organization has achieved all the specific and generic goals of the maturity level 2 process areas. In other words, the projects of the organization have ensured that requirements are managed and that processes are planned, performed, measured, and controlled. The process discipline reflected by maturity level 2 helps to ensure that existing practices are retained during times of stress. When these practices are in place, projects are performed and managed according to their documented plans. At maturity level 2, requirements, processes, work products, and services are managed. The status of the work products and the delivery of services are visible to management at defined points. 147

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Commitments are established among relevant stakeholders and are revised as needed. Work products are reviewed with stakeholders and are controlled. The work products and services satisfy their specified requirements, standards, and objectives. Maturity Level 3 - Defined At maturity level 3, an organization has achieved all the specific and generic goals of the process areas assigned to maturity levels 2 and 3. At maturity level 3, processes are well characterized and understood, and are described in standards, procedures, tools, and methods. A critical distinction between maturity level 2 and maturity level 3 is the scope of standards, process descriptions, and procedures. At maturity level 2, the standards, process descriptions, and procedures may be quite different in each specific instance of the process (for example, on a particular project). At maturity level 3, the standards, process descriptions, and procedures for a project are tailored from the organization's set of standard processes to suit a particular project or organizational unit. The organization's set of standard processes includes the processes addressed at maturity level 2 and maturity level 3. As a result, the processes that are performed across the organization are consistent except for the differences allowed by the tailoring guidelines. Another critical distinction is that at maturity level 3, processes are typically described in more detail and more rigorously than at maturity level 2. At maturity level 3, processes are managed more proactively using an understanding of the interrelationships of the process activities and detailed measures of the process, its work products, and its services. Maturity Level 4 - Quantitatively Managed At maturity level 4, an organization has achieved all the specific goals of the process areas assigned to maturity levels 2, 3, and 4 and the generic goals assigned to maturity levels 2 and 3. At maturity level 4 Subprocesses are selected that significantly contribute to overall process performance. These selected subprocesses are controlled using statistical and other quantitative techniques. Quantitative objectives for quality and process performance are established and used as criteria in managing processes. Quantitative objectives are based on the needs of the customer, end users, organization, and process implementers. Quality and process performance are understood in statistical terms and are managed throughout the life of the processes. For these processes, detailed measures of process performance are collected and statistically analyzed. Special causes of process variation are identified and, where appropriate, the sources of special causes are corrected to prevent future occurrences. Quality and process performance measures are incorporated into the organization.s measurement repository to support fact-based decision making in the future. 148

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A critical distinction between maturity level 3 and maturity level 4 is the predictability of process performance. At maturity level 4, the performance of processes is controlled using statistical and other quantitative techniques, and is quantitatively predictable. At maturity level 3, processes are only qualitatively predictable. Maturity Level 5 - Optimizing At maturity level 5, an organization has achieved all the specific goals of the process areas assigned to maturity levels 2, 3, 4, and 5 and the generic goals assigned to maturity levels 2 and 3. Processes are continually improved based on a quantitative understanding of the common causes of variation inherent in processes. Maturity level 5 focuses on continually improving process performance through both incremental and innovative technological improvements. Quantitative process-improvement objectives for the organization are established, continually revised to reflect changing business objectives, and used as criteria in managing process improvement. The effects of deployed process improvements are measured and evaluated against the quantitative process-improvement objectives. Both the defined processes and the organization's set of standard processes are targets of measurable improvement activities. Optimizing processes that are agile and innovative depends on the participation of an empowered workforce aligned with the business values and objectives of the organization. The organization's ability to rapidly respond to changes and opportunities is enhanced by finding ways to accelerate and share learning. Improvement of the processes is inherently part of everybody's role, resulting in a cycle of continual improvement. A critical distinction between maturity level 4 and maturity level 5 is the type of process variation addressed. At maturity level 4, processes are concerned with addressing special causes of process variation and providing statistical predictability of the results. Though processes may produce predictable results, the results may be insufficient to achieve the established objectives. At maturity level 5, processes are concerned with addressing common causes of process variation and changing the process (that is, shifting the mean of the process performance) to improve process performance (while maintaining statistical predictability) to achieve the established quantitative process-improvement objectives. Maturity Levels Should Not be Skipped: Each maturity level provides a necessary foundation for effective implementation of processes at the next level. Higher level processes have less chance of success without the discipline provided by lower levels. The effect of innovation can be obscured in a noisy process. Higher maturity level processes may be performed by organizations at lower maturity levels, with the risk of not being consistently applied in a crisis. 149

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Maturity Levels and Process Areas: Here is a list of all the corresponding process areas defined for a S/W organization. These process areas may be different for different organization. This section is just giving names of the related process areas, for more detail about these Process Areas go through CMMI Process Areas Chapter. Level

5 Optimizing

Focus

Continuous Improvement

Key Area

Process

Organizational Innovation and Process Deployment Causal Analysis and Resolution Organizational Process Performance Quantitative Project Management

4 Quantitatively Managed

Quantitatively Managed

3 Defined

Requirements Development Technical Solution Product Integration Verification Validation Organizational Process Standardization Process Focus Organizational Process Definition Organizational Training Integrated Project Mgmt (with IPPD extras) Risk

Result

Highest Quality / Lowest Risk Higher Quality / Lower Risk

Medium Quality / Medium Risk

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Management Decision Analysis and Resolution Integrated Teaming (IPPD only) Org. Environment for Integration (IPPD only) Integrated Supplier Management (SS only)

2 Managed

1 Initial

Basic Management

Requirements Management Project Planning Project Monitoring and Control Supplier Project Agreement Management Measurement and Analysis Process and Product Quality Assurance Configuration Management

Process is informal and Adhoc

Low Quality / High Risk

Lowest Quality / Highest Risk

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5.10 Six Sigma Six Sigma has following two key ethodologies: DMAIC: refers to a data-driven quality strategy for improving processes. his methodology is used to improve an existing business process. DMADV: refers to a data-driven quality strategy for designing products & processes. This methodology is used to create ne product designs or process designs in such a way that it results in a more predictable, mature and defect free performance. There is one more methodology c lled DFSS - Design For Six Sigma. DF S is a data-driven quality strategy for designing desi n or re-design a product or service from the ground up. Sometimes a DMAIC project ma turn into a DFSS project because the p ocess in question requires complete redesign to brin about the desired degree of improveme t. DMAIC Methodology: This methodology consists of foll wing five steps. Define --> Measure --> Analyze --> Improve -->Control Define : Define the Problem or Pr ject Goals that needs to be addressed. Measure: Measure the problem a d process from which it was produced. Analyze: Analyze data & process to determine root causes of defects and opportunities. Improve: Improve the process b finding solutions to fix, diminish, a d prevent future problems. Control: Implement, Control, an Sustain the improvements solutions to keep the process on the new course.

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DMAIC is a data-driven quality strategy used to improve processes. It is a integral part of a Six Sigma initiative, but in genera can be implemented as a standalone qu lity improvement procedure or as part of other p ocess improvement initiatives such as lean. DMAIC is an acronym for the five phases that make up the process: Define the problem, improve ent activity, opportunity for improvement, the project goal , and customer (internal and external) requirements. Measure process performance. Analyze the process to determine root causes of variation, poor performance (defects). Improve process performance y addressing and eliminating the root causes. Control the improved process and future process performance. The DMAIC process easily lends itself to the project approach to quality improvement encouraged and promoted by Juran.

Cp7301 Software Process and Project Management Notes

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