Software engineering a practitioners approach 8th edition pressman solutions ...Drusilla918
Full clear download( no error formatting) at: https://goo.gl/XmRyGP
software engineering a practitioner's approach 8th edition pdf free download
software engineering a practitioner's approach 8th edition ppt
software engineering a practitioner's approach 6th edition pdf
software engineering pressman 9th edition pdf
software engineering a practitioner's approach 9th edition
software engineering a practitioner's approach 9th edition pdf
software engineering a practitioner's approach 7th edition solution manual pdf
roger s. pressman
The COCOMO model is a software cost estimation model that allows inputting parameters to estimate the effort required for a software project. It was developed considering the waterfall process and software developed from scratch. There are three modes of development - organic, semi-detached, and embedded - based on complexity. The model also consists of basic, intermediate, and detailed forms with varying levels of accuracy. The intermediate model uses 15 cost drivers while the detailed model divides the software into modules and applies COCOMO to each.
1. Software project estimation involves decomposing a project into smaller problems like major functions and activities. Estimates can be based on similar past projects, decomposition techniques, or empirical models.
2. Accurate estimates depend on properly estimating the size of the software product using techniques like lines of code, function points, or standard components. Baseline metrics from past projects are then applied to the size estimates.
3. Decomposition techniques involve estimating the effort needed for each task or function and combining them. Process-based estimation decomposes the software process into tasks while problem-based estimation decomposes the problem.
This document discusses different types of intelligent agents. It describes four basic types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based only on the current percept, while model-based reflex agents maintain an internal model of the world. Goal-based agents use goals to determine desirable situations. Utility-based agents maximize an internal utility function that represents the performance measure. The document also discusses agent functions, percepts, environments, and the PEAS properties of task environments.
The document discusses concepts related to software project scheduling, including:
- Software project scheduling involves distributing estimated effort across the planned project duration by allocating effort to specific tasks.
- There are two perspectives on software scheduling - either working within a prescribed end date or setting the end date based on the software team's estimates.
- Basic principles of software scheduling include compartmentalizing tasks, determining dependencies, allocating time estimates, validating effort, and defining responsibilities, outcomes, and milestones.
- Tracking project schedules involves comparing actual progress to planned schedules through status meetings, reviews, and milestone completions.
The document discusses software estimation and project planning. It covers estimating project cost and effort through decomposition techniques and empirical estimation models. Specifically, it discusses:
1) Decomposition techniques involve breaking down a project into functions and tasks to estimate individually, such as estimating lines of code or function points for each piece.
2) Empirical estimation models use historical data from past projects to generate estimates.
3) Key factors that affect estimation accuracy include properly estimating product size, translating size to effort/time/cost, and accounting for team abilities and requirements stability.
This document discusses different software estimation techniques. It describes what software estimation is, why it is needed, and some common difficulties in estimation. It then outlines factors to consider like product objectives, corporate assets, and project constraints. It discusses methods for estimating lines of code or function points. Function point analysis and the unadjusted and value adjustment components are explained. Models for calculating effort and cost using lines of code and function points are provided, including the COCOMO model and its organic, semi-detached, and embedded project types.
Process models are not perfect, but provide road map for software engineering work. Software models provide stability, control, and organization to a process that if not managed can easily get out of control
Software process models are adapted to meet the needs of software engineers and managers for a specific project.
Software engineering a practitioners approach 8th edition pressman solutions ...Drusilla918
Full clear download( no error formatting) at: https://goo.gl/XmRyGP
software engineering a practitioner's approach 8th edition pdf free download
software engineering a practitioner's approach 8th edition ppt
software engineering a practitioner's approach 6th edition pdf
software engineering pressman 9th edition pdf
software engineering a practitioner's approach 9th edition
software engineering a practitioner's approach 9th edition pdf
software engineering a practitioner's approach 7th edition solution manual pdf
roger s. pressman
The COCOMO model is a software cost estimation model that allows inputting parameters to estimate the effort required for a software project. It was developed considering the waterfall process and software developed from scratch. There are three modes of development - organic, semi-detached, and embedded - based on complexity. The model also consists of basic, intermediate, and detailed forms with varying levels of accuracy. The intermediate model uses 15 cost drivers while the detailed model divides the software into modules and applies COCOMO to each.
1. Software project estimation involves decomposing a project into smaller problems like major functions and activities. Estimates can be based on similar past projects, decomposition techniques, or empirical models.
2. Accurate estimates depend on properly estimating the size of the software product using techniques like lines of code, function points, or standard components. Baseline metrics from past projects are then applied to the size estimates.
3. Decomposition techniques involve estimating the effort needed for each task or function and combining them. Process-based estimation decomposes the software process into tasks while problem-based estimation decomposes the problem.
This document discusses different types of intelligent agents. It describes four basic types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based only on the current percept, while model-based reflex agents maintain an internal model of the world. Goal-based agents use goals to determine desirable situations. Utility-based agents maximize an internal utility function that represents the performance measure. The document also discusses agent functions, percepts, environments, and the PEAS properties of task environments.
The document discusses concepts related to software project scheduling, including:
- Software project scheduling involves distributing estimated effort across the planned project duration by allocating effort to specific tasks.
- There are two perspectives on software scheduling - either working within a prescribed end date or setting the end date based on the software team's estimates.
- Basic principles of software scheduling include compartmentalizing tasks, determining dependencies, allocating time estimates, validating effort, and defining responsibilities, outcomes, and milestones.
- Tracking project schedules involves comparing actual progress to planned schedules through status meetings, reviews, and milestone completions.
The document discusses software estimation and project planning. It covers estimating project cost and effort through decomposition techniques and empirical estimation models. Specifically, it discusses:
1) Decomposition techniques involve breaking down a project into functions and tasks to estimate individually, such as estimating lines of code or function points for each piece.
2) Empirical estimation models use historical data from past projects to generate estimates.
3) Key factors that affect estimation accuracy include properly estimating product size, translating size to effort/time/cost, and accounting for team abilities and requirements stability.
This document discusses different software estimation techniques. It describes what software estimation is, why it is needed, and some common difficulties in estimation. It then outlines factors to consider like product objectives, corporate assets, and project constraints. It discusses methods for estimating lines of code or function points. Function point analysis and the unadjusted and value adjustment components are explained. Models for calculating effort and cost using lines of code and function points are provided, including the COCOMO model and its organic, semi-detached, and embedded project types.
Process models are not perfect, but provide road map for software engineering work. Software models provide stability, control, and organization to a process that if not managed can easily get out of control
Software process models are adapted to meet the needs of software engineers and managers for a specific project.
Presentation covers all aspects about Software Designing that are followed by Software Engineering Industries. Readers can do detailed study about the Software Design Concepts like (Abstraction, Architecture, Patterns, Modularity, Information Hiding, Refinement, Functional Dependence, Cohesion, Coupling & Refactoring) plus Design Process.
Later then Design Principles are there to understand with Architectural Design, Architectural Styles, Data Centered Architecture, Data Flow Architecture, Call & Return Architecture, Object Oriented Architecture, Layered Architecture with other architectures are named at end of it.
Later then, Component Level Design is discussed. Then after UI Design & Rules of it, UI Design Models, Web Application Design, WebApp Interface Design are discussed at the end.
Comment back if you have any query about it.
The document discusses software requirements and requirements engineering. It introduces concepts like user requirements, system requirements, functional requirements, and non-functional requirements. It explains how requirements can be organized in a requirements document and the different types of stakeholders who read requirements. The document also discusses challenges in writing requirements precisely and provides examples of requirements specification for a library system called LIBSYS.
The document discusses requirements analysis and analysis modeling principles for software engineering. It covers key topics such as:
1. Requirements analysis specifies a software's operational characteristics and interface with other systems to establish constraints. Analysis modeling focuses on what the software needs to do, not how it will be implemented.
2. Analysis modeling principles include representing the information domain, defining functions, modeling behavior, partitioning complex problems, and moving from essential information to implementation details.
3. Common analysis techniques involve use case diagrams, class diagrams, state diagrams, data flow diagrams, and data modeling to define attributes, relationships, cardinality and modality between data objects.
Software Cost Estimation in Software Engineering SE23koolkampus
Software cost estimation involves predicting resources required for development using metrics like productivity, size, and function points. The COCOMO 2 model is an empirical algorithmic model that estimates effort as a function of product, project, and process attributes. It operates at three levels - early prototyping based on object points, early design using function points mapped to lines of code, and post-architecture directly using lines of code. Key cost drivers include requirements, personnel experience, reuse, and development flexibility.
The document discusses database schema refinement through normalization. It introduces the concepts of functional dependencies and normal forms including 1NF, 2NF, 3NF and BCNF. Decomposition is presented as a technique to resolve issues like redundancy, update anomalies and insertion/deletion anomalies that arise due to violations of normal forms. Reasoning about functional dependencies and computing their closure is also covered.
source code metrics and other maintenance tools and techniquesSiva Priya
The document discusses two source code metrics: Halstead's effort equation and McCabe's cyclomatic complexity measure. Halstead's metrics are based on counts of operators, operands, unique operators, and unique operands in source code. McCabe's measure defines the complexity of a program's control flow graph based on the number of edges, nodes, and connected components. The document also mentions that software maintenance involves a range of activities from code modification to tracking complexity metrics over time.
Rumbaugh's Object Modeling Technique (OMT) is an object-oriented analysis and design methodology. It uses three main modeling approaches: object models, dynamic models, and functional models. The object model defines the structure of objects in the system through class diagrams. The dynamic model describes object behavior over time using state diagrams and event flow diagrams. The functional model represents system processes and data flow using data flow diagrams.
The data design action translates data objects into data structures at the software component level.
Data Design is the first and most important design activity. Here the main issue is to select the appropriate data structure i.e. the data design focuses on the definition of data structures.
Data design is a process of gradual refinement, from the coarse "What data does your application require?" to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
The Unified Process (UP) is a popular iterative software development framework that uses use cases, architecture-centric design, and the Unified Modeling Language. It originated from Jacobson's Objectory process in the 1980s and was further developed by Rational Software into the Rational Unified Process. The UP consists of four phases - inception, elaboration, construction, and transition - and emphasizes iterative development, architectural drivers, and risk-managed delivery.
The document discusses the software design process. It begins by explaining that software design is an iterative process that translates requirements into a blueprint for constructing the software. It then describes the main steps and outputs of the design process, which include transforming specifications into design models, reviewing designs for quality, and producing a design document. The document also covers key concepts in software design like abstraction, architecture, patterns, modularity, and information hiding.
Software reliability is defined as the probability of failure-free operation of software over a specified time period and environment. Key factors influencing reliability include fault count, which is impacted by code size/complexity and development processes, and operational profile, which describes how users operate the system. Software reliability methodologies aim to improve dependability through fault avoidance, tolerance, removal, and forecasting, with the latter using models to predict reliability mathematically based on factors like time between failures or failure counts.
This document discusses software process models. It defines a software process as a framework for activities required to build high-quality software. A process model describes the phases in a product's lifetime from initial idea to final use. The document then describes a generic process model with five framework activities - communication, planning, modeling, construction, and deployment. It provides an example of identifying task sets for different sized projects. Finally, it discusses the waterfall process model as the first published model, outlining its sequential phases and problems with being rarely linear and requiring all requirements up front.
The document provides an overview of software cost estimation, outlining various methods used including algorithmic models like COCOMO, expert judgement, top-down and bottom-up approaches, and estimation by analogy. It discusses COCOMO in detail, including the original COCOMO 81 model and updated COCOMO II model, and emphasizes the importance of calibration for accurate estimates.
This document provides an overview of a requirements specification (SRS) for a software engineering project. It defines what an SRS is, its purpose, types of requirements it should include, its typical structure, characteristics of a good SRS, and benefits of developing an SRS. The SRS is intended to clearly define the requirements for a software product to guide its design and development.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
The document discusses project planning in software engineering. It defines project planning and its importance. It describes the project manager's responsibilities which include project planning, reporting, risk management, and people management. It discusses challenges in software project planning. The RUP process for project planning is then outlined which involves creating artifacts like the business case and software development plan. Risk management is also a key part of project planning.
Software project planning involves defining roles and responsibilities, ensuring work aligns with business objectives, and checking schedules and requirements feasibility. It requires risk analysis, tracking the project plan, and meeting quality standards. Issues can include unclear requirements, time/budget mismanagement, personnel problems, and lack of management support. Key activities are identifying requirements, estimating costs/risks, preparing a project charter and plan, and commencing the project. The master schedule summarizes deliverables and milestones based on a master project plan and detailed work schedules.
The document contains slides from a lecture on software engineering. It discusses definitions of software and software engineering, different types of software applications, characteristics of web applications, and general principles of software engineering practice. The slides are copyrighted and intended for educational use as supplementary material for a textbook on software engineering.
Line of Code (LOC) Matric and Function Point MatricAnkush Singh
This document provides an overview of two popular software metrics: lines of code (LOC) and function points. It defines LOC as a measure of the size of a computer program by counting the number of lines in its source code, excluding comments and headers. LOC can be physical (including blank lines and comments) or logical (executable statements only). Function points measure software size by categorizing its functional user requirements into inputs, outputs, inquiries, internal files, and external interfaces, then calculating an unadjusted function point value based on their sum. Both metrics aim to objectively and quantitatively estimate the size and effort of a software project.
The document discusses different types of software metrics that can be used to measure various aspects of software development. Process metrics measure attributes of the development process, while product metrics measure attributes of the software product. Project metrics are used to monitor and control software projects. Metrics need to be normalized to allow for comparison between different projects or teams. This can be done using size-oriented metrics that relate measures to the size of the software, or function-oriented metrics that relate measures to the functionality delivered.
Presentation covers all aspects about Software Designing that are followed by Software Engineering Industries. Readers can do detailed study about the Software Design Concepts like (Abstraction, Architecture, Patterns, Modularity, Information Hiding, Refinement, Functional Dependence, Cohesion, Coupling & Refactoring) plus Design Process.
Later then Design Principles are there to understand with Architectural Design, Architectural Styles, Data Centered Architecture, Data Flow Architecture, Call & Return Architecture, Object Oriented Architecture, Layered Architecture with other architectures are named at end of it.
Later then, Component Level Design is discussed. Then after UI Design & Rules of it, UI Design Models, Web Application Design, WebApp Interface Design are discussed at the end.
Comment back if you have any query about it.
The document discusses software requirements and requirements engineering. It introduces concepts like user requirements, system requirements, functional requirements, and non-functional requirements. It explains how requirements can be organized in a requirements document and the different types of stakeholders who read requirements. The document also discusses challenges in writing requirements precisely and provides examples of requirements specification for a library system called LIBSYS.
The document discusses requirements analysis and analysis modeling principles for software engineering. It covers key topics such as:
1. Requirements analysis specifies a software's operational characteristics and interface with other systems to establish constraints. Analysis modeling focuses on what the software needs to do, not how it will be implemented.
2. Analysis modeling principles include representing the information domain, defining functions, modeling behavior, partitioning complex problems, and moving from essential information to implementation details.
3. Common analysis techniques involve use case diagrams, class diagrams, state diagrams, data flow diagrams, and data modeling to define attributes, relationships, cardinality and modality between data objects.
Software Cost Estimation in Software Engineering SE23koolkampus
Software cost estimation involves predicting resources required for development using metrics like productivity, size, and function points. The COCOMO 2 model is an empirical algorithmic model that estimates effort as a function of product, project, and process attributes. It operates at three levels - early prototyping based on object points, early design using function points mapped to lines of code, and post-architecture directly using lines of code. Key cost drivers include requirements, personnel experience, reuse, and development flexibility.
The document discusses database schema refinement through normalization. It introduces the concepts of functional dependencies and normal forms including 1NF, 2NF, 3NF and BCNF. Decomposition is presented as a technique to resolve issues like redundancy, update anomalies and insertion/deletion anomalies that arise due to violations of normal forms. Reasoning about functional dependencies and computing their closure is also covered.
source code metrics and other maintenance tools and techniquesSiva Priya
The document discusses two source code metrics: Halstead's effort equation and McCabe's cyclomatic complexity measure. Halstead's metrics are based on counts of operators, operands, unique operators, and unique operands in source code. McCabe's measure defines the complexity of a program's control flow graph based on the number of edges, nodes, and connected components. The document also mentions that software maintenance involves a range of activities from code modification to tracking complexity metrics over time.
Rumbaugh's Object Modeling Technique (OMT) is an object-oriented analysis and design methodology. It uses three main modeling approaches: object models, dynamic models, and functional models. The object model defines the structure of objects in the system through class diagrams. The dynamic model describes object behavior over time using state diagrams and event flow diagrams. The functional model represents system processes and data flow using data flow diagrams.
The data design action translates data objects into data structures at the software component level.
Data Design is the first and most important design activity. Here the main issue is to select the appropriate data structure i.e. the data design focuses on the definition of data structures.
Data design is a process of gradual refinement, from the coarse "What data does your application require?" to the precise data structures and processes that provide it. With a good data design, your application's data access is fast, easily maintained, and can gracefully accept future data enhancements.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
The Unified Process (UP) is a popular iterative software development framework that uses use cases, architecture-centric design, and the Unified Modeling Language. It originated from Jacobson's Objectory process in the 1980s and was further developed by Rational Software into the Rational Unified Process. The UP consists of four phases - inception, elaboration, construction, and transition - and emphasizes iterative development, architectural drivers, and risk-managed delivery.
The document discusses the software design process. It begins by explaining that software design is an iterative process that translates requirements into a blueprint for constructing the software. It then describes the main steps and outputs of the design process, which include transforming specifications into design models, reviewing designs for quality, and producing a design document. The document also covers key concepts in software design like abstraction, architecture, patterns, modularity, and information hiding.
Software reliability is defined as the probability of failure-free operation of software over a specified time period and environment. Key factors influencing reliability include fault count, which is impacted by code size/complexity and development processes, and operational profile, which describes how users operate the system. Software reliability methodologies aim to improve dependability through fault avoidance, tolerance, removal, and forecasting, with the latter using models to predict reliability mathematically based on factors like time between failures or failure counts.
This document discusses software process models. It defines a software process as a framework for activities required to build high-quality software. A process model describes the phases in a product's lifetime from initial idea to final use. The document then describes a generic process model with five framework activities - communication, planning, modeling, construction, and deployment. It provides an example of identifying task sets for different sized projects. Finally, it discusses the waterfall process model as the first published model, outlining its sequential phases and problems with being rarely linear and requiring all requirements up front.
The document provides an overview of software cost estimation, outlining various methods used including algorithmic models like COCOMO, expert judgement, top-down and bottom-up approaches, and estimation by analogy. It discusses COCOMO in detail, including the original COCOMO 81 model and updated COCOMO II model, and emphasizes the importance of calibration for accurate estimates.
This document provides an overview of a requirements specification (SRS) for a software engineering project. It defines what an SRS is, its purpose, types of requirements it should include, its typical structure, characteristics of a good SRS, and benefits of developing an SRS. The SRS is intended to clearly define the requirements for a software product to guide its design and development.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
The document discusses project planning in software engineering. It defines project planning and its importance. It describes the project manager's responsibilities which include project planning, reporting, risk management, and people management. It discusses challenges in software project planning. The RUP process for project planning is then outlined which involves creating artifacts like the business case and software development plan. Risk management is also a key part of project planning.
Software project planning involves defining roles and responsibilities, ensuring work aligns with business objectives, and checking schedules and requirements feasibility. It requires risk analysis, tracking the project plan, and meeting quality standards. Issues can include unclear requirements, time/budget mismanagement, personnel problems, and lack of management support. Key activities are identifying requirements, estimating costs/risks, preparing a project charter and plan, and commencing the project. The master schedule summarizes deliverables and milestones based on a master project plan and detailed work schedules.
The document contains slides from a lecture on software engineering. It discusses definitions of software and software engineering, different types of software applications, characteristics of web applications, and general principles of software engineering practice. The slides are copyrighted and intended for educational use as supplementary material for a textbook on software engineering.
Line of Code (LOC) Matric and Function Point MatricAnkush Singh
This document provides an overview of two popular software metrics: lines of code (LOC) and function points. It defines LOC as a measure of the size of a computer program by counting the number of lines in its source code, excluding comments and headers. LOC can be physical (including blank lines and comments) or logical (executable statements only). Function points measure software size by categorizing its functional user requirements into inputs, outputs, inquiries, internal files, and external interfaces, then calculating an unadjusted function point value based on their sum. Both metrics aim to objectively and quantitatively estimate the size and effort of a software project.
The document discusses different types of software metrics that can be used to measure various aspects of software development. Process metrics measure attributes of the development process, while product metrics measure attributes of the software product. Project metrics are used to monitor and control software projects. Metrics need to be normalized to allow for comparison between different projects or teams. This can be done using size-oriented metrics that relate measures to the size of the software, or function-oriented metrics that relate measures to the functionality delivered.
Function point analysis is a method of estimating the size of a software application based on the user view rather than lines of code. It involves identifying and classifying functional components such as internal logical files, external interface files, inputs, outputs, and inquiries. Each component is assigned a complexity and weight to calculate the total functional size in function points. The size can then be adjusted based on 14 general system characteristics to determine the final adjusted size. The document provides details on the history, vocabulary, types of data and transactions, counting process, and complexity determination involved in function point analysis.
The document discusses improving project management processes. It describes projects, project management, and processes. There are nine key project management processes: scope, schedule, budget, quality, team, stakeholder, information, risk, and contract management. These processes can be improved through measurement, analysis, and change. Process improvement focuses on reducing defects, costs, and schedules. Continuous improvement is important to keep up with competition.
This document discusses different types of work breakdown structures that can be used for project planning and management. These include task-oriented WBS that focus on verbs and deliverables, time-phased WBS that track work in phases, and organizational or geographically-focused WBS. It also mentions cost-breakdown structures.
This document discusses software project management. It begins by defining project management and its goals of supporting smooth development and reducing problems. It then discusses the four key aspects of effective software project management: people, product, process, and project. For each of these, it provides details on important considerations and best practices. It also discusses project planning, monitoring and control, termination. Finally, it defines important terms related to metrics and measurements for software projects.
This document discusses software project management. It begins by defining project management and its goals of supporting smooth development and reducing problems. It then discusses the four key aspects of effective software project management: people, product, process, and project. For each of these, it provides details on important considerations and best practices. It also discusses project planning, monitoring and control, termination. Key activities covered in depth include effort estimation, metrics, and measurements.
Projects and project management processes vary from industry to industry; however, these are more traditional elements of a project. The overarching goal is typically to offer a product, change a process or to solve a problem in order to benefit the organization.
The document discusses function points as a standardized way to measure software productivity and size. It argues that software companies should know how much software they produce through formal measurement programs. Function points count the number of inputs, outputs, inquiries and files to objectively measure the size of software and allow for accurate estimation of costs, schedules and productivity. While challenging to implement, formal software measurement programs using function points provide many benefits for software organizations.
This presentation contains the fundamentals of Function point Analysis. There are plenty of examples included in this presentation and one can learn the concepts using these examples.
The document discusses work breakdown structures (WBS), which break down project tasks into hierarchical packages. A WBS splits tasks into logical packages that can be cost controlled and reported on. The lowest level of a WBS should be individual tasks that a single person can complete within a reasonable time period. However, a WBS does not show task dependencies, durations, or resources. Alternatives to a WBS include a product breakdown structure, which breaks down project components, and a cost breakdown structure, which identifies cost categories and should be done after the WBS and PBS.
A contracting and project management firm, The Dalton Company has been involved with significant projects for nonprofits, including Toronto’s Wychwood Barns. Using ArtsBuild’s online guide to capital projects PLAN IT | BUILD IT, The Dalton Company discusses the planning and decision-making needed to deliver projects on budget and in time.
This document presents information on cost estimation using the COCOMO model. It discusses the basic, intermediate, and detailed COCOMO models. The basic model uses effort multipliers, staff size, and productivity equations to estimate effort and schedule for projects of different modes (organic, embedded, semidetached). The intermediate model adds 15 cost drivers to improve accuracy. The detailed model incorporates three product levels, phase-sensitive effort multipliers, and effort/time fractions for each development phase.
COEPD - Center of Excellence for Professional Development is a primarily a Business Analyst Training Institute in the IT industry of India head quartered at Hyderabad. COEPD is expert in Business Analyst Training in Hyderabad, Chennai, Pune , Mumbai & Vizag. We offer Business Analyst Training with affordable prices that fit your needs.
COEPD conducts 4-day workshops throughout the year for all participants in various locations i.e. Hyderabad, Pune. The workshops are also conducted on Saturdays and Sundays for the convenience of working professionals.
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What exactly is the role of a project manager? There are lot of responsibilities that a project manager has to fulfill. Here is a high level overview of the expectations from the project manager.
Function Point Analysis & Cocomo. Two main estimation methods for structured and object oriented methodology estimations. Cocomo is widely used in estimating where Rational Unified Process is followed.
The document discusses software project planning and size estimation techniques. It describes lines of code counting, function point analysis, and the process for calculating unadjusted function points and complexity adjustment factors. Function point analysis involves identifying functional components and assigning weighted counts and complexity levels. The counts are then used to calculate the unadjusted function point total, which is adjusted based on complexity factors to determine the final function point estimate.
The document provides an overview of software sizing and function point analysis (FPA). It discusses the need for software sizing to estimate size and manage projects. It introduces common sizing methodologies like lines of code and use cases. The bulk of the document then focuses on explaining FPA, including defining what a function point is, categorizing functional requirements into base components, assigning complexity ratings and counts, and determining an adjusted function point count using value adjustment factors.
COCOMO I is a software cost estimation model published in 1981 by Barry Boehm. It uses a waterfall lifecycle approach and estimates development effort as a function of program size (measured in KDSI) and 15 cost drivers. The model has three levels - basic, intermediate, and detailed - with the detailed version incorporating impacts on each development phase. While transparent, it is difficult to accurately estimate size early on and vulnerable to misclassifying development mode. Success relies on tuning the model using organizational historical data.
This document discusses different types of software metrics including process, product, and project metrics. It defines metrics as quantitative measures of attributes and discusses how they can be used as indicators to improve processes and projects. Process metrics measure attributes of the development process over long periods of time. Product metrics measure attributes of the software at different stages. Project metrics are used to monitor and control projects. The document also discusses size-oriented and function-oriented metrics for normalization and comparison purposes. It provides examples of calculating function points and deriving metrics like errors per function point.
This document discusses different types of software metrics that can be used to measure and evaluate software projects and processes. It defines key terms like measure, measurement, and metric. It explains that metrics are used to indicate quality, assess productivity, evaluate new methods/tools, and form baselines for estimation. The main types of metrics discussed are process metrics, which measure the development process, and project metrics, which are used to monitor and control software projects. Examples of different metrics include lines of code, defects, cost, effort, size-oriented metrics, and function-oriented metrics. The document provides details on calculating and applying function points as a type of function-oriented metric.
Se 381 - lec 25 - 32 - 12 may29 - program size and cost estimation modelsbabak danyal
The document discusses various techniques for estimating software project size, effort, cost and duration. It describes lines of code and function points as common metrics for estimating project size. Function points measure the number of inputs, outputs, inquiries and files to estimate size early in development. The document also explains the COCOMO model for estimating effort, productivity, duration and staffing needs based on project size, complexity and other cost drivers. Intermediate COCOMO incorporates 15 cost drivers while basic COCOMO uses only size. References are provided for further reading.
Software metrics can be used to measure various attributes of software products and processes. There are direct metrics that immediately measure attributes like lines of code and defects, and indirect metrics that measure less tangible aspects like functionality and reliability. Metrics are classified as product metrics, which measure attributes of the software product, and process metrics, which measure the software development process. Project metrics are used tactically within a project to track status, risks, and quality, while process metrics are used strategically for long-term process improvement. Common software quality attributes that can be measured include correctness, maintainability, integrity, and usability.
This document provides an overview of software cost estimation. It discusses various techniques for estimating software costs such as lines of code models, function point models, and COCOMO models. The key points covered include:
- Software cost estimation is needed for pricing and budgeting software projects. However, software development is risky with many projects going over budget.
- Techniques like LOC, function points, and COCOMO models use various metrics like lines of code, functionality measures, and complexity factors to estimate effort, schedule and costs.
- Factors like programmer productivity, complexity adjustments, and project characteristics impact cost estimates. Function point analysis assigns weights based on simple, average or complex attributes of user inputs, outputs, files and
This document discusses metrics that can be used to measure software processes and projects. It begins by defining software metrics and explaining that they provide quantitative measures that offer insight for improving processes and projects. It then distinguishes between metrics for the software process domain and project domain. Process metrics are collected across multiple projects for strategic decisions, while project metrics enable tactical project management. The document outlines various metric types, including size-based metrics using lines of code or function points, quality metrics, and metrics for defect removal efficiency. It emphasizes integrating metrics into the software process through establishing a baseline, collecting data, and providing feedback to facilitate continuous process improvement.
This document discusses software metrics for processes, projects, and products. It defines metrics as quantitative measures used as management tools to provide insight. Metrics in the process domain are used for strategic decisions, while project metrics enable tactical decisions. Size-oriented metrics normalize measures by lines of code or function points. Function-oriented metrics use functionality as a normalization value. Quality metrics measure correctness and maintainability. Establishing a metrics baseline from past projects allows for process, product, and project improvements.
The document discusses software cost estimation techniques, specifically the COCOMO model. It describes the COCOMO model's approach of estimating software size, effort, duration and cost through three stages - basic, intermediate, and complete COCOMO. The basic COCOMO model provides equations to estimate effort in person-months and development time in months based on the estimated source code size (KLOC) for different product categories - organic, semi-detached, and embedded. Examples are provided to demonstrate how to apply the basic COCOMO equations.
The document discusses software cost estimation techniques, specifically the COCOMO model. It describes the COCOMO model's approach of estimating software size, effort, duration and cost through three stages - basic, intermediate, and complete COCOMO. The basic COCOMO model provides equations to estimate effort in person-months and development time in months based on the estimated source code size (KLOC) for different product categories - organic, semi-detached, and embedded. Examples are provided to demonstrate how to apply the basic COCOMO equations.
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSneha Padhiar
The document discusses software cost estimation techniques, specifically the COCOMO model. It describes the COCOMO model's approach of estimating software size, effort, duration and cost through three stages - basic, intermediate, and complete COCOMO. The basic COCOMO model uses lines of code (KLOC) to estimate effort in person-months and development time in months based on constants that vary for organic, semi-detached and embedded product types. An example application for an organic project is provided. Function point analysis is also introduced as an alternative to lines of code for size estimation.
This document discusses software metrics and measurement. It describes how measurement can be used throughout the software development process to assist with estimation, quality control, productivity assessment, and project control. It defines key terms like measures, metrics, and indicators and explains how they provide insight into the software process and product. The document also discusses using metrics to evaluate and improve the software process as well as track project status, risks, and quality. Finally, it covers different types of metrics like size-oriented, function-oriented, and quality metrics.
This document discusses various techniques for estimating software projects, including decomposition techniques like software sizing based on lines of code or function points. It also covers estimating using problem-based decomposition into functions and estimating with use cases. Metrics for process, product, and project are discussed, as well as size-oriented, function-oriented, object-oriented, and use case-oriented metrics. Function point calculation and components are explained in detail.
The document discusses techniques for decomposing software projects to aid in cost estimation. It describes decomposing by problem or process. Process decomposition breaks down framework activities like communication. For complex projects, communication can be broken into smaller tasks. The document also discusses software sizing methods, empirical estimation models, and making buy versus build decisions. It outlines manual and automated cost estimation techniques from project-level to activity-level estimates.
The document discusses different techniques for configuring virtual hosting on a server. It describes IP-based virtual hosting where each domain has a unique IP address. Port-based virtual hosting uses different ports to host multiple websites. Name-based virtual hosting is the most common technique, using a single IP address and the domain name to determine which website to serve.
The document discusses software project planning and cost estimation. It covers the 4Ps model of project planning - product, process, people, and project. It then discusses various software size and cost estimation techniques, including lines of code, function points analysis, heuristic models like COCOMO, and empirical and analytical estimation approaches. COCOMO is described as one of the most commonly used software estimation models, predicting effort and schedule based on size.
Software metricsIntroduction
Attributes of Software Metrics
Activities of a Measurement Process
Types
Normalization of Metrics
Help software engineers to gain insight into the design and construction of the software
Activities of a Measurement Process
To answer this we need to know the size & complexity of the projects.
But if we normalize the measures, it is possible to compare the two
For normalization we have 2 ways-
Size-Oriented Metrics
Function Oriented Metrics
The document discusses software measurement and metrics. It defines software measurement as quantifying attributes of software products and processes. Metrics are used to measure software quality levels. There are different types of metrics including product, process, and project metrics. Common software metrics include lines of code, function points, and complexity measures. Metrics should be quantitative, understandable, repeatable, and economical to compute.
This document discusses software metrics and function point analysis. It provides information on different types of software metrics including size-oriented, function-oriented, and attribute metrics. It then focuses on explaining function point analysis in detail. Function point analysis measures software size based on user requirements rather than lines of code. It involves counting various functional components and adjusting for complexity. The document provides steps to calculate unadjusted and adjusted function points for different examples. Overall, the document provides a comprehensive overview of software metrics with a focus on function point analysis methodology.
Algorithmic software cost modeling uses mathematical functions to estimate project costs based on inputs like project characteristics, development processes, and product attributes. COCOMO is a widely used algorithmic cost modeling method that estimates effort in person-months and development time based on source lines of code and cost adjustment factors. It has basic, intermediate, and detailed models and accounts for factors like application domain experience, process quality, and technology changes.
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Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
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people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
2. Terminology
• Measure: Quantitative indication of the extent, amount, dimension, or size of
some attribute of a product or process.
• Metrics: The degree to which a system, component, or process possesses a
given attribute. Relates several measures (e.g. average number of errors found
per person hour)
• Indicators: A combination of metrics that provides insight into the software
process, project or product
• Direct Metrics: Immediately measurable attributes (e.g. line of code, execution
speed, defects reported)
• Indirect Metrics: Aspects that are not immediately quantifiable (e.g.
functionality, quantity, reliability)
• Faults:
− Errors: Faults found by the practitioners during software development
− Defects: Faults found by the customers after release
3. Why Measure Software?
• Determine quality of the current product or
process
• Predict qualities of a product/process
• Improve quality of a product/process
4. Example Metrics
• Defects rates
• Errors rates
• Measured by:
– individual
– module
– during development
• Errors should be categorized by origin, type, cost
5. Metric Classification
• Products
– Explicit results of software development activities.
– Deliverables, documentation, by products
• Processes
– Activities related to production of software
• Project
– Inputs into the software development activities
– hardware, knowledge, people
6. Product vs. Process
• Process Metrics-
– Insights of process paradigm, software engineering tasks, work
product, or milestones.
– Lead to long term process improvement.
• Product Metrics-
– Assesses the state of the project
– Track potential risks
– Uncover problem areas
– Adjust workflow or tasks
– Evaluate teams ability to control quality
7. Process Metrics
• Focus on quality achieved as a consequence of a repeatable
or managed process. Strategic and Long Term.
• Statistical Software Process Improvement (SSPI). Error
Categorization and Analysis:
All errors and defects are categorized by origin
The cost to correct each error and defect is recorded
The number of errors and defects in each category is computed
Data is analyzed to find categories that result in the highest cost to the
organization
Plans are developed to modify the process
• Defect Removal Efficiency (DRE). Relationship between
errors (E) and defects (D). The ideal is a DRE of 1:
)/( DEEDRE +=
8. Project Metrics
• Used by a project manager and software team to adapt
project work flow and technical activities. Tactical and Short
Term.
• Purpose:
− Minimize the development schedule by making the
necessary adjustments to avoid delays and mitigate
problems
− Assess product quality on an ongoing basis
• Metrics:
− Effort or time per SE task
− Errors uncovered per review hour
− Scheduled vs. actual milestone dates
− Number of changes and their characteristics
− Distribution of effort on SE tasks
9. Product Metrics
• Focus on the quality of deliverables
• Product metrics are combined across several projects to
produce process metrics
• Metrics for the product:
− Measures of the Analysis Model
− Complexity of the Design Model
1. Internal algorithmic complexity
2. Architectural complexity
3. Data flow complexity
− Code metrics
11. Size Oriented Metrics
• Size of the software produced
• Lines Of Code (LOC)
• 1000 Lines Of Code KLOC
• Effort measured in person months
• Errors/KLOC
• Defects/KLOC
• Cost/LOC
• Documentation Pages/KLOC
• LOC is programmer & language dependent
12. LOC Metrics
• Easy to use
• Easy to compute
• Can compute LOC of existing systems but
•cost and requirements traceability may be lost
• Language & programmer dependent
13. Function Point Metrics
• Function point metrics provide a standardized method
for measuring the various functions of a software
application.
• Function point metrics, measure functionality from the
users point of view, that is, on the basis of what the user
requests and receives in return
14. Function Point Metrics
• Number of user inputs
– Distinct input from user
• Number of user outputs
– Reports, screens, error messages, etc
• Number of user inquiries
– On line input that generates some result
• Number of files
– Logical file (database)
• Number of external interfaces
– Data files/connections as interface to other systems
15. Compute Function Points
• FP = Total Count * [0.65 + 0.01*∑(Fi)]
• Total count is all the counts times a weighting factor that
is determined for each organization via empirical data
• Fi (i=1 to 14) are complexity adjustment values
17. Function Point Analysis
A simple example:
inputs
3 simple X 3 = 6
4 average X 4 = 16
1 complex X 6 = 6
outputs
6 average X 5 = 30
2 complex X 7 = 14
files
5 complex X 15 = 75
inquiries
8 average X 4 = 32
interfaces
3 average X 7 = 21
4 complex X 10 = 40
Unadjusted function points 240
18. Value Adjustment Factors
F1. Data Communication
F2. Distributed Data Processing
F3. Performance
F4. Heavily Used Configuration
F5. Transaction Role
F6. Online Data Entry
F7. End-User Efficiency
F8. Online Update
F9. Complex Processing
F10. Reusability
F11. Installation Ease
F12. Operational Ease
F13. Multiple Sites
F14. Facilitate Change
19. Function Point Analysis
Continuing our example . . .
Complex internal processing = 3
Code to be reusable = 2
High performance = 4
Multiple sites = 3
Distributed processing = 5
Project adjustment factor = 17
Adjustment calculation:
Adjusted FP = Unadjusted FP X [0.65 + (adjustment factor X 0.01)]
= 240 X [0.65 + ( 17 X 0.01)]
= 240 X [0.82]
= 197 Adjusted function points
20. Compute Function Points Example-1
Study of requirement specification for a project has produced
following results:
Need for 7 inputs, 10 outputs, 6 inquiries, 17 files and 4 external
interfaces.
Input and external interface function point attributes are of average
complexity and all other function points attributes are of low
complexity
Determine adjusted function points assuming complexity adjustment
value is 32.
•Total count = 233
•Fp = 226.01
21. Compute Function Points Example-2
GTU- May, Dec 2013
Compute the function points for the given data set
Inputs = 8
Outputs = 12
Inquiries= 4
Logical files = 41
Interfaces = 1
Value adjustment factor ∑Fi= 41
•Total count = 525
•Fp = 556.5
22. Compute Function Points Example-3
A system has 12 external inputs, 24 external outputs, fields
30 different external queries, manages 4 internal logical
files, and interfaces with 6 different legacy systems (6 EIFs).
All of these data are of average complexity and assume
that all complexity adjustment values are average.
Compute FP for the system.
23. Compute Function Points Example-4
Compute the function points for the given data set
Inputs = 32
Outputs = 60
Inquiries= 24
Logical files = 8
Interfaces = 2
Assume that all complexity adjustment values are average
24. Software Project Estimation
Decomposing
• Software project estimation is a form of problem solving,
and in most cases, the problem to be solved is too
complex to be considered in one piece.
• For this reason, decomposing the problem, re-
characterizing it as a set of smaller problems.
• Before an estimate can be made, the project planner
must understand the scope of the software to be built
and generate an estimate of its "size."
25. Software Project Estimation
• Software Sizing
• Problem based Estimation
– LOC based
– FP based
• Process based Estimation
• Estimation with Use-cases
26. Introduction
• Before an estimate can be made and decomposition
techniques applied, the planner must
– Understand the scope of the software to be built
– Generate an estimate of the software’s size
• Then one of two approaches are used
– Problem-based estimation
• Based on either source lines of code or function point estimates
– Process-based estimation
• Based on the effort required to accomplish each task
27. Approaches to Software
Sizing
• Function point sizing
– Develop estimates of the information domain characteristics (Ch. 15 –
Product Metrics for Software)
• Standard component sizing
– Estimate the number of occurrences of each standard component
– Use historical project data to determine the delivered LOC size per
standard component
• Change sizing
– Used when changes are being made to existing software
– Estimate the number and type of modifications that must be
accomplished
– Types of modifications include reuse, adding code, changing code,
and deleting code
– An effort ratio is then used to estimate each type of change and the
size of the change
28. Problem-Based Estimation
1) Start with a bounded statement of scope
2) Decompose the software into problem functions that can each
be estimated individually
3) Compute an LOC or FP value for each function
4) Derive cost or effort estimates by applying the LOC or FP
values to your baseline productivity metrics (e.g., LOC/person-
month or FP/person-month)
5) Combine function estimates to produce an overall estimate for
the entire project
29. Problem-Based Estimation
(continued)
• In general, the LOC/pm and FP/pm metrics should be computed by
project domain
– Important factors are team size, application area, and complexity
• LOC and FP estimation differ in the level of detail required for
decomposition with each value
– For LOC, decomposition of functions is essential and should go into
considerable detail (the more detail, the more accurate the estimate)
– For FP, decomposition occurs for the five information domain
characteristics and the 14 adjustment factors
• External inputs, external outputs, external inquiries, internal logical files,
external interface files
pm = person month
30. Problem-Based Estimation
(continued)
• For both approaches, the planner uses lessons learned to
estimate an optimistic, most likely, and pessimistic size value
for each function or count (for each information domain
value)
• Then the expected size value S is computed as follows:
S = (Sopt + 4Sm + Spess)/6
• Historical LOC or FP data is then compared to S in order to
cross-check it
31. Process-Based Estimation
Obtained from “process framework”Obtained from “process framework”
application
functions
framework activitiesframework activities
Effort required to
accomplish
each framework
activity for each
application
function
32. Process-Based Estimation
1) Identify the set of functions that the software needs to
perform as obtained from the project scope
2) Identify the series of framework activities that need to be
performed for each function
3) Estimate the effort (in person months) that will be required
to accomplish each software process activity for each
function
33. Process-Based Estimation
(continued)
4) Apply average labor rates (i.e., cost/unit effort) to the effort
estimated for each process activity
5) Compute the total cost and effort for each function and
each framework activity
6) Compare the resulting values to those obtained by way of
the LOC and FP estimates
• If both sets of estimates agree, then your numbers are
highly reliable
• Otherwise, conduct further investigation and analysis
concerning the function and activity breakdown
This is the most commonly used of the two estimation techniques (problem and process)
36. SOLC
• The project size helps determine the resources, effort,
and duration of the project.
• SOLC is defined as the source lines of code that are
delivered as part of the product.
• The effort spent on creating the source lines of code is
expressed in relation to thousand lines of code (KLOC).
• This technique includes the calculation of lines of code,
documentation of pages, inputs, outputs, and
components of a software program.
• The SLOC technique is language-dependent. The effort
required to calculate
37. COCOMO
• Stands for COnstructive COst MOdel
• Introduced by Barry Boehm in 1981
• Became one of the well-known and widely-used estimation
models in the industry
• It has evolved into a more comprehensive estimation model
called COCOMO II
• COCOMO II is actually a hierarchy of three estimation models
• As with all estimation models, it requires sizing information
and accepts it in three forms: object points, function points,
and lines of source code
38. COCOMO Models
• Application composition model - Used during the early stages
of software engineering when the following are important
– Prototyping of user interfaces
– Consideration of software and system interaction
– Assessment of performance
– Evaluation of technology maturity
• Early design stage model – Used once requirements have been
stabilized and basic software architecture has been established
• Post-architecture stage model – Used during the construction
of the software
39. Organic, Semidetached and
Embedded software projects
• Organic: A development project can be considered of organic
type, if the project deals with developing a well understood
application program, the size of the development team is
reasonably small, and the team members are experienced in
developing similar types of projects.
• Semidetached: A development project can be considered of
semidetached type, if the development consists of a mixture
of experienced and inexperienced staff. Team members may
have limited experience on related systems but may be
unfamiliar with some aspects of the system being developed.
• Embedded: A development project is considered to be of
embedded type, if the software being developed is strongly
coupled to complex hardware, or if the stringent regulations
on the operational procedures exist.
43. Project Scheduling & Tracking
• Software project scheduling is an action that
distributes estimated effort across the
planned project duration by allocating the
effort to specific software engineering tasks.
44. Scheduling Principles - 1
• Compartmentalization
– the product and process must be decomposed into a manageable
number of activities and tasks
• Interdependency
– tasks that can be completed in parallel must be separated from
those that must completed serially
• Time allocation
– every task has start and completion dates that take the task
interdependencies into account
45. Scheduling Principles - 2
• Effort validation
– project manager must ensure that on any given
day there are enough staff members assigned to
completed the tasks within the time estimated in
the project plan
• Defined Responsibilities
– every scheduled task needs to be assigned to a
specific team member
46. Scheduling Principles - 3
• Defined outcomes
– every task in the schedule needs to have a defined
outcome (usually a work product or deliverable)
• Defined milestones
– a milestone is accomplished when one or more
work products from an engineering task have
passed quality review
47. Effort Distribution
• general guideline - 40-20-40 rule
• 40% or more of all effort allocated to analysis and
design tasks
• 40% of effort allocated to testing
• 20% of effort allocated to programming
• characteristics of each project dictate the
distribution of effort
48. 48
Project Effort Distribution
Generally accepted guidelines are:
02-03 % planning
10-25 % requirements analysis
20-25 % design
15-20 % coding
30-40 % testing and debugging
49. Project types:
• concept development projects
– initiated to explore some new business concept or
application of new technology
• new application development
– undertaken due to specific customer request
• application enhancement
• application maintenance
• reengineering
– rebuild existing legacy system
50. Software Project Types - 1
• Concept development
– initiated to explore new business concept or new application of
technology
• New application development
– new product requested by customer
• Application enhancement
– major modifications to function, performance, or interfaces
(observable to user)
51. Software Project Types - 2
• Application maintenance
– correcting, adapting, or extending existing
software (not immediately obvious to user)
• Reengineering
– rebuilding all (or part) of a legacy system
53. Scheduling
Scheduling of a software project does not differ greatly from scheduling of any multitask
engineering effort.
Two project scheduling methods:
- Program Evaluation and Review Technique (PERT)
- Critical Path Method (CPM)
Both methods are driven by information developed in earlier project planning activities:
- Estimates of effort
- A decomposition of product function
- The selection of the appropriate process model
- The selection of project type and task set
Both methods allow a planer to do:
- determine the critical path
- time estimation
- calculate boundary times for each task
Boundary times:
- the earliest time and latest time to begin a task
- the earliest time and latest time to complete a task
- the total float.
54. Tracking the Schedule
he project schedule provides a road map for a software project manager.
defines the tasks and milestones.
everal ways to track a project schedule:
- conducting periodic project status meeting
- evaluating the review results in the software process
- determine if formal project milestones have been accomplished
- compare actual start date to planned start date for each task
- informal meeting with practitioners
roject manager takes the control of the schedule in the aspects of:
- project staffing
- project problems
- project resources
- reviews
- project budget
56. Definition of Risk
• A risk is a potential problem – it might happen and it might
not
• Conceptual definition of risk
– Risk concerns future happenings
– Risk involves change in mind, opinion, actions, places, etc.
– Risk involves choice and the uncertainty that choice
entails
• Two characteristics of risk
– Uncertainty – the risk may or may not happen, that is,
there are no 100% risks (those, instead, are called
constraints)
– Loss – the risk becomes a reality and unwanted
consequences or losses occur
57. Risk Categorization – Approach #1
• Project risks
– They threaten the project plan
– If they become real, it is likely that the project schedule will
slip and that costs will increase
• Technical risks
– They threaten the quality and timeliness of the software to
be produced
– If they become real, implementation may become difficult
or impossible
• Business risks
– They threaten the feasibility of the software to be built
– If they become real, they threaten the project or the
product
58. Risk Categorization – Approach #1
• Sub-categories of Business risks
– Market risk – building an excellent product or system that no
one really wants
– Strategic risk – building a product that no longer fits into the
overall business strategy for the company
– Sales risk – building a product that the sales force doesn't
understand how to sell
– Management risk – losing the support of senior management
due to a change in focus or a change in people
– Budget risk – losing budgetary or personnel commitment
59. Risk Categorization – Approach #2
• Known risks
– Those risks that can be uncovered after careful evaluation of
the project plan, the business and technical environment in
which the project is being developed, and other reliable
information sources (e.g., unrealistic delivery date)
• Predictable risks
– Those risks that are deduced from past project experience
(e.g., past turnover)
• Unpredictable risks
– Those risks that can and do occur, but are extremely difficult
to identify in advance
60. Reactive vs. Proactive Risk Strategies
• Reactive risk strategies
– "Don't worry, I'll think of something"
– The majority of software teams and managers rely on this
approach
– Nothing is done about risks until something goes wrong
• The team then flies into action in an attempt to correct the problem
rapidly (fire fighting)
– Crisis management is the choice of management techniques
• Proactive risk strategies
– Steps for risk management are followed
– Primary objective is to avoid risk and to have a emergency
plan in place to handle unavoidable risks in a controlled and
effective manner
61. Steps for Risk Management
1) Identify possible risks; recognize what can go wrong
2) Analyze each risk to estimate the probability that it will
occur and the impact (i.e., damage) that it will do if it does
occur
3) Rank the risks by probability and impact
- Impact may be negligible, marginal, critical, and
catastrophic
4) Develop a contingency plan to manage those risks having
high probability and high impact
63. Background
• Risk identification is a systematic attempt to specify threats to the project
plan
• By identifying known and predictable risks, the project manager takes a first
step toward avoiding them when possible and controlling them when
necessary
• Generic risks
– Risks that are a potential threat to every software project
• Product-specific risks
– Risks that can be identified only by those a with a clear understanding of
the technology, the people, and the environment that is specific to the
software that is to be built
– This requires examination of the project plan and the statement of scope
– "What special characteristics of this product may threaten our project
plan?"
64. Known and Predictable Risk Categories
• Product size – risks associated with overall size of the software to be
built
• Business impact – risks associated with constraints imposed by
management or the marketplace
• Customer characteristics – risks associated with sophistication of the
customer and the developer's ability to communicate with the customer
in a timely manner
• Process definition – risks associated with the degree to which the
software process has been defined and is followed
• Development environment – risks associated with availability and
quality of the tools to be used to build the project
• Technology to be built – risks associated with complexity of the system
to be built and the "newness" of the technology in the system
• Staff size and experience – risks associated with overall technical and
project experience of the software engineers who will do the work
66. Background
• Risk projection (or estimation) attempts to rate each risk in
two ways
– The probability that the risk is real
– The consequence of the problems associated with the risk,
should it occur
67. Risk Projection/Estimation Steps
1) Establish a scale that reflects the perceived likelihood of a
risk (e.g., 1-low, 10-high)
2) Explain the consequences of the risk
3) Estimate the impact of the risk on the project and product
4) Note the overall accuracy of the risk projection so that there
will be no misunderstandings
69. Background
• An effective strategy for dealing with risk must consider
three issues
(Note: these are not mutually exclusive)
– Risk mitigation (i.e., avoidance)
– Risk monitoring
– Risk management and contingency planning
• Risk mitigation (avoidance) is the primary strategy and is
achieved through a plan
– Example: Risk of high staff turnover
70. Seven Principles of Risk
Management• Maintain a global perspective
– View software risks within the context of a system and the business
problem that is is intended to solve
• Take a forward-looking view
– Think about risks that may arise in the future; establish contingency
plans
• Encourage open communication
– Encourage all stakeholders and users to point out risks at any time
• Integrate risk management
– Integrate the consideration of risk into the software process
• Emphasize a continuous process of risk management
– Modify identified risks as more becomes known and add new risks as
better insight is achieved
• Develop a shared product vision
– A shared vision by all stakeholders facilitates better risk identification
and assessment
• Encourage teamwork when managing risk
– Pool the skills and experience of all stakeholders when conducting risk
management activities