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.
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.
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.
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.
This ppt covers the following
A strategic approach to testing
Test strategies for conventional software
Test strategies for object-oriented software
Validation testing
System testing
The art of debugging
The document discusses several key challenges in software engineering (SE). It notes that SE approaches must address issues of scale, productivity, and quality. Regarding scale, it states that SE methods must be scalable for problems of different sizes, from small to very large, requiring both engineering and project management techniques to be formalized for large problems. Productivity is important to control costs and schedule, and SE aims to deliver high productivity. Quality is also a major goal, involving attributes like functionality, reliability, usability, efficiency and maintainability. Reliability is often seen as the main quality criterion and is approximated by measuring defects. Addressing these challenges of scale, productivity and quality drives the selection of SE approaches.
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.
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.
Risk management involves identifying potential problems, assessing their likelihood and impacts, and developing strategies to address them. There are two main risk strategies - reactive, which addresses risks after issues arise, and proactive, which plans ahead. Key steps in proactive risk management include identifying risks through checklists, estimating their probability and impacts, developing mitigation plans, monitoring risks and mitigation effectiveness, and adjusting plans as needed. Common risk categories include project risks, technical risks, and business risks.
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.
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.
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.
This ppt covers the following
A strategic approach to testing
Test strategies for conventional software
Test strategies for object-oriented software
Validation testing
System testing
The art of debugging
The document discusses several key challenges in software engineering (SE). It notes that SE approaches must address issues of scale, productivity, and quality. Regarding scale, it states that SE methods must be scalable for problems of different sizes, from small to very large, requiring both engineering and project management techniques to be formalized for large problems. Productivity is important to control costs and schedule, and SE aims to deliver high productivity. Quality is also a major goal, involving attributes like functionality, reliability, usability, efficiency and maintainability. Reliability is often seen as the main quality criterion and is approximated by measuring defects. Addressing these challenges of scale, productivity and quality drives the selection of SE approaches.
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.
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.
Risk management involves identifying potential problems, assessing their likelihood and impacts, and developing strategies to address them. There are two main risk strategies - reactive, which addresses risks after issues arise, and proactive, which plans ahead. Key steps in proactive risk management include identifying risks through checklists, estimating their probability and impacts, developing mitigation plans, monitoring risks and mitigation effectiveness, and adjusting plans as needed. Common risk categories include project risks, technical risks, and business risks.
The document discusses important concepts for effective software project management including focusing on people, product, process, and project. It emphasizes that defining project scope and establishing clear objectives at the beginning of a project are critical first steps. Finally, it outlines factors for selecting an appropriate software development process model and adapting it to the specific project.
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.
Agile development focuses on effective communication, customer collaboration, and incremental delivery of working software. The key principles of agile development according to the Agile Alliance include satisfying customers, welcoming changing requirements, frequent delivery, collaboration between business and development teams, and self-organizing teams. Extreme Programming (XP) is an agile process model that emphasizes planning with user stories, simple design, pair programming, unit testing, and frequent integration and testing.
This document discusses different process models used in software development. It describes the key phases and characteristics of several common process models including waterfall, prototyping, V-model, incremental, iterative, spiral and agile development models. The waterfall model involves sequential phases from requirements to maintenance without iteration. Prototyping allows for user feedback earlier. The V-model adds verification and validation phases. Incremental and iterative models divide the work into smaller chunks to allow for iteration and user feedback throughout development.
This document discusses the nature of software. It defines software as a set of instructions that can be stored electronically. Software engineering encompasses processes and methods to build high quality computer software. Software has a dual role as both a product and a vehicle to deliver products. Characteristics of software include being engineered rather than manufactured, and not wearing out over time like hardware. Software application domains include system software, application software, engineering/scientific software, embedded software, product-line software, web applications, and artificial intelligence software. The document also discusses challenges like open-world computing and legacy software.
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.
This document discusses 15 factors that influence quality and productivity in software development processes: individual ability, team communication, product complexity, appropriate notations, systematic approaches, change control, level of technology, level of reliability, problem understanding, available time, required skills, facilities and resources, adequacy of training, management skills, and appropriate goals. Each factor is described in 1-3 paragraphs on how it can impact quality and productivity.
The document discusses several prescriptive software process models including:
1) The waterfall model which follows sequential phases from requirements to deployment but lacks iteration.
2) The incremental model which delivers functionality in increments with each phase repeated.
3) Prototyping which focuses on visible aspects to refine requirements through iterative prototypes and feedback.
4) The RAD (Rapid Application Development) model which emphasizes very short development cycles of 60-90 days using parallel teams and automated tools. The document provides descriptions and diagrams of each model.
This document outlines the typical sections and contents of a software requirements specification (SRS). It discusses 12 common sections of an SRS, including an overview, development environments, external interfaces, functional requirements, performance requirements, exception handling, implementation priorities, foreseeable changes, acceptance criteria, design guidance, a cross-reference index, and a glossary. Key sections describe functional requirements using relational or state-oriented notation, performance characteristics like response times, and exception handling categories. The SRS should have properties like being correct, complete, consistent, unambiguous, functional, and verifiable.
This document discusses software engineering and software quality assurance. It begins by defining software and describing a case study on the Therac-25 radiation therapy machine which suffered from a software failure disaster. It then covers classification of causes of software errors, definitions of software quality from IEEE and Pressman, and objectives of SQA activities. Key causes of errors listed include faulty requirements, client-developer communication failures, deliberate deviations from requirements, logical design errors, coding errors, non-compliance with documentation, shortcomings in testing, procedure errors, and documentation errors. The document also discusses definitions of quality assurance and quality control and the goals of SQA in software development and maintenance.
Coupling refers to the interdependence between software modules. There are several types of coupling from loose to tight, with the tightest being content coupling where one module relies on the internal workings of another. Cohesion measures how strongly related the functionality within a module is, ranging from coincidental to functional cohesion which is the strongest. Tight coupling and low cohesion can make software harder to maintain and reuse modules.
Verification and Validation in Software Engineering SE19koolkampus
The document introduces software verification and validation (V&V) and discusses key techniques used in the V&V process, including inspections, static analysis, and the Cleanroom development process. It defines verification as ensuring a product is built correctly and validation as ensuring the right product is built. V&V aims to find defects and assess usability, applying techniques from requirements through deployment. Inspections and static analysis complement testing by checking static representations, while testing checks dynamic behavior. The Cleanroom process uses formal specification, incremental development, and statistical testing with reliability models.
What is Quality ||
Software Quality Metrics ||
Types of Software Quality Metrics ||
Three groups of Software Quality Metrics ||
Customer Satisfaction Metrics ||
Tools used for Quality Metrics/Measurements ||
PERT and CPM ||
The document provides an overview of the Software Engineering course for the second semester of the second year (B.Tech IT/II Sem-II). It includes details about the term, text books, unit syllabus, index of topics, and slides covering introductions to software engineering, the changing nature of software, software myths, generic views of process, the Capability Maturity Model Integration and personal and team software processes.
Evolutionary process models allow developers to iteratively create increasingly complete versions of software. Examples include the prototyping paradigm, spiral model, and concurrent development model. The prototyping paradigm uses prototypes to elicit requirements from customers. The spiral model couples iterative prototyping with controlled development, dividing the project into framework activities. The concurrent development model concurrently develops components with defined interfaces to enable integration. These evolutionary models allow flexibility and accommodate changes but require strong communication and updated requirements.
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 maintenance typically requires 40-60% of the total lifecycle effort for a software product, with some cases requiring as much as 90%. A widely used rule of thumb is that maintenance activities are distributed as 60% for enhancements, 20% for adaptations, and 20% for corrections. Studies show the typical level of effort devoted to software maintenance is around 50% of the total lifecycle effort. Boehm suggests measuring maintenance effort using an activity ratio that considers the number of instructions added or modified over the total instructions. The effort required can then be estimated using programmer months based on the activity ratio and an effort adjustment factor. Emphasis on reliability during development can reduce future maintenance effort.
The document discusses software process models which define a structured set of activities for developing software systems. These activities typically include specification, design & implementation, validation, and evolution. Process models provide organization and stability to software development. They define the approach taken and include activities like communication, planning, modeling, construction, and deployment. Process models can have different flows like linear, iterative, or evolutionary and can address problems at different levels of abstraction through patterns. Process assessment methods help ensure processes meet criteria for successful software engineering.
The document discusses factors related to software project size and effort. It provides the following key points:
1) Software development and maintenance can account for a significant portion of economic activity, with estimates that it will account for 12.5% of the US GDP by 1990.
2) Most effort is spent on maintenance rather than development, with estimates that maintenance accounts for 60-90% of total effort.
3) Software project size is categorized based on factors like number of programmers, duration, lines of code, and interactions/complexity. These range from trivial single-programmer projects to extremely large projects involving thousands of programmers over 5-10 years.
4) A 1964 study found that programmers only spent
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 important concepts for effective software project management including focusing on people, product, process, and project. It emphasizes that defining project scope and establishing clear objectives at the beginning of a project are critical first steps. Finally, it outlines factors for selecting an appropriate software development process model and adapting it to the specific project.
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.
Agile development focuses on effective communication, customer collaboration, and incremental delivery of working software. The key principles of agile development according to the Agile Alliance include satisfying customers, welcoming changing requirements, frequent delivery, collaboration between business and development teams, and self-organizing teams. Extreme Programming (XP) is an agile process model that emphasizes planning with user stories, simple design, pair programming, unit testing, and frequent integration and testing.
This document discusses different process models used in software development. It describes the key phases and characteristics of several common process models including waterfall, prototyping, V-model, incremental, iterative, spiral and agile development models. The waterfall model involves sequential phases from requirements to maintenance without iteration. Prototyping allows for user feedback earlier. The V-model adds verification and validation phases. Incremental and iterative models divide the work into smaller chunks to allow for iteration and user feedback throughout development.
This document discusses the nature of software. It defines software as a set of instructions that can be stored electronically. Software engineering encompasses processes and methods to build high quality computer software. Software has a dual role as both a product and a vehicle to deliver products. Characteristics of software include being engineered rather than manufactured, and not wearing out over time like hardware. Software application domains include system software, application software, engineering/scientific software, embedded software, product-line software, web applications, and artificial intelligence software. The document also discusses challenges like open-world computing and legacy software.
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.
This document discusses 15 factors that influence quality and productivity in software development processes: individual ability, team communication, product complexity, appropriate notations, systematic approaches, change control, level of technology, level of reliability, problem understanding, available time, required skills, facilities and resources, adequacy of training, management skills, and appropriate goals. Each factor is described in 1-3 paragraphs on how it can impact quality and productivity.
The document discusses several prescriptive software process models including:
1) The waterfall model which follows sequential phases from requirements to deployment but lacks iteration.
2) The incremental model which delivers functionality in increments with each phase repeated.
3) Prototyping which focuses on visible aspects to refine requirements through iterative prototypes and feedback.
4) The RAD (Rapid Application Development) model which emphasizes very short development cycles of 60-90 days using parallel teams and automated tools. The document provides descriptions and diagrams of each model.
This document outlines the typical sections and contents of a software requirements specification (SRS). It discusses 12 common sections of an SRS, including an overview, development environments, external interfaces, functional requirements, performance requirements, exception handling, implementation priorities, foreseeable changes, acceptance criteria, design guidance, a cross-reference index, and a glossary. Key sections describe functional requirements using relational or state-oriented notation, performance characteristics like response times, and exception handling categories. The SRS should have properties like being correct, complete, consistent, unambiguous, functional, and verifiable.
This document discusses software engineering and software quality assurance. It begins by defining software and describing a case study on the Therac-25 radiation therapy machine which suffered from a software failure disaster. It then covers classification of causes of software errors, definitions of software quality from IEEE and Pressman, and objectives of SQA activities. Key causes of errors listed include faulty requirements, client-developer communication failures, deliberate deviations from requirements, logical design errors, coding errors, non-compliance with documentation, shortcomings in testing, procedure errors, and documentation errors. The document also discusses definitions of quality assurance and quality control and the goals of SQA in software development and maintenance.
Coupling refers to the interdependence between software modules. There are several types of coupling from loose to tight, with the tightest being content coupling where one module relies on the internal workings of another. Cohesion measures how strongly related the functionality within a module is, ranging from coincidental to functional cohesion which is the strongest. Tight coupling and low cohesion can make software harder to maintain and reuse modules.
Verification and Validation in Software Engineering SE19koolkampus
The document introduces software verification and validation (V&V) and discusses key techniques used in the V&V process, including inspections, static analysis, and the Cleanroom development process. It defines verification as ensuring a product is built correctly and validation as ensuring the right product is built. V&V aims to find defects and assess usability, applying techniques from requirements through deployment. Inspections and static analysis complement testing by checking static representations, while testing checks dynamic behavior. The Cleanroom process uses formal specification, incremental development, and statistical testing with reliability models.
What is Quality ||
Software Quality Metrics ||
Types of Software Quality Metrics ||
Three groups of Software Quality Metrics ||
Customer Satisfaction Metrics ||
Tools used for Quality Metrics/Measurements ||
PERT and CPM ||
The document provides an overview of the Software Engineering course for the second semester of the second year (B.Tech IT/II Sem-II). It includes details about the term, text books, unit syllabus, index of topics, and slides covering introductions to software engineering, the changing nature of software, software myths, generic views of process, the Capability Maturity Model Integration and personal and team software processes.
Evolutionary process models allow developers to iteratively create increasingly complete versions of software. Examples include the prototyping paradigm, spiral model, and concurrent development model. The prototyping paradigm uses prototypes to elicit requirements from customers. The spiral model couples iterative prototyping with controlled development, dividing the project into framework activities. The concurrent development model concurrently develops components with defined interfaces to enable integration. These evolutionary models allow flexibility and accommodate changes but require strong communication and updated requirements.
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 maintenance typically requires 40-60% of the total lifecycle effort for a software product, with some cases requiring as much as 90%. A widely used rule of thumb is that maintenance activities are distributed as 60% for enhancements, 20% for adaptations, and 20% for corrections. Studies show the typical level of effort devoted to software maintenance is around 50% of the total lifecycle effort. Boehm suggests measuring maintenance effort using an activity ratio that considers the number of instructions added or modified over the total instructions. The effort required can then be estimated using programmer months based on the activity ratio and an effort adjustment factor. Emphasis on reliability during development can reduce future maintenance effort.
The document discusses software process models which define a structured set of activities for developing software systems. These activities typically include specification, design & implementation, validation, and evolution. Process models provide organization and stability to software development. They define the approach taken and include activities like communication, planning, modeling, construction, and deployment. Process models can have different flows like linear, iterative, or evolutionary and can address problems at different levels of abstraction through patterns. Process assessment methods help ensure processes meet criteria for successful software engineering.
The document discusses factors related to software project size and effort. It provides the following key points:
1) Software development and maintenance can account for a significant portion of economic activity, with estimates that it will account for 12.5% of the US GDP by 1990.
2) Most effort is spent on maintenance rather than development, with estimates that maintenance accounts for 60-90% of total effort.
3) Software project size is categorized based on factors like number of programmers, duration, lines of code, and interactions/complexity. These range from trivial single-programmer projects to extremely large projects involving thousands of programmers over 5-10 years.
4) A 1964 study found that programmers only spent
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.
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 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.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
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.
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 various software project size estimation metrics. It describes the limitations of lines of code (LOC) counting, such as variability due to coding style and not accounting for non-coding effort. Function point analysis and feature point analysis are presented as alternatives that overcome some LOC limitations by basing size on software features rather than lines. The key steps of function point analysis involve counting types of inputs, outputs, inquiries and other parameters to calculate unadjusted function points which are then adjusted based on technical complexity factors. While more accurate than LOC, function point analysis is still subjective based on how parameters are defined and counted.
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.
Activities During Software Project Management, Process For Successful Projects, categories of functional units, Counting function points, Computing function points
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.
1. Software project planning involves decomposing the project into smaller problems, estimating the size and effort required, and creating schedules and plans for quality, risks, and monitoring.
2. Size estimation is important for project planning and can be done through direct methods like lines of code or indirect methods like function points. The results from different sizing methods are combined statistically.
3. Estimation involves decomposing the software into problem functions, estimating each individually, and combining them to estimate the overall project using historical productivity metrics.
This is short review of project matrices. This short lecture provides an overview that how software project matrices help software project manager to make accurate estimates.
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.
Lecture 8 (software Metrics) Unit 3.pptxironman427662
This document summarizes a lecture on software metrics. It defines software metrics and indicators. It describes the seven core metrics used in managing software projects: work and progress, budgeted cost and expenditures, staffing and team dynamics, change traffic and stability, breakage and modularity, rework and adaptability, and mean time between failures. It also discusses the three management indicators of technical progress, financial status, and staffing progress.
Chapter 11 Metrics for process and projects.pptssuser3f82c9
This document discusses software process and project metrics. It describes two types of metrics - process metrics and project metrics. Process metrics are collected across projects over long periods of time to enable long-term process improvement. Project metrics enable project managers to assess project status, track risks, uncover problems, adjust work, and evaluate team ability. Measurement data is collected by projects and converted to process metrics for software improvement.
The document discusses various aspects of software project management including the management scope, people, product, process, and project. It also covers software metrics which are quantitative measures used to gain insight into software characteristics and processes. Examples of metrics include lines of code, defects, and customer problems to measure aspects like size, quality, and satisfaction.
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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This document provides an overview of requirements documentation and modeling techniques. It discusses guidelines for writing requirements, such as using standard templates and natural language. Requirements documents establish what a system should do and provide validation. The document also discusses use case modeling and defines actors, flows of events, and extensions. It provides an example case study of an ATM banking system and describes associated use cases. Finally, it discusses principles of modeling like abstraction and partitioning, as well as modeling techniques like object-oriented and functional modeling.
The document provides requirements for developing software for an automated teller machine (ATM) banking system. It outlines the key functions of the system, including validating customer cards and PINs, performing withdrawal, balance inquiry, and funds transfer transactions, and maintaining transaction records. Diagrams are included in appendices to illustrate use cases, system objects, and data flow. The software is intended to enable customers to securely conduct basic banking activities from distributed ATMs connected to a central server.
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This document discusses quality attributes of requirements documents and software requirements specifications (SRS). It outlines what should be included in an SRS, such as functional and non-functional requirements, as well as what should not be included. The document then describes key quality attributes an SRS should have, such as being correct, unambiguous, complete, verifiable, consistent, understandable, modifiable, traced, traceable, design independent, annotated, concise and organized. Examples are provided for some attributes.
Requirements management is the process of managing changes to system requirements. This lecture discusses reasons for changing requirements and how to manage them. Requirements cannot be effectively managed without traceability between requirements and other project artifacts. Requirements will inevitably change for reasons like evolving stakeholder needs, environmental changes, and technical issues. There are both stable and volatile requirements, with volatile types including mutable, emergent, consequential, and compatibility requirements.
This document discusses requirements documents, also known as software requirements specifications (SRS). It explains that an SRS is used to formally communicate system requirements to stakeholders. The document outlines what an SRS typically includes, such as user requirements, system constraints, and technical definitions. It also describes common SRS sections based on the IEEE standard, such as an introduction, general description, and specific requirements. Finally, it notes that the structure of an SRS depends on factors like the system type and organizational practices.
Requirements elicitation involves understanding the application domain, problem to be solved, organizational needs, and stakeholder requirements. The key stages of elicitation are setting objectives, acquiring background knowledge, organizing knowledge, and collecting stakeholder requirements. Elicitation requires techniques like interviewing, scenarios, and prototyping to understand stakeholders. Elicitation, analysis, and negotiation are iterative processes that inform one another to refine requirements.
Requirements elicitation involves gathering requirements through techniques like interviews, focus groups, and meetings. It aims to discover the needs of users and stakeholders but faces challenges like unclear scopes, differing understandings between parties, and changing requirements over time. The requirements elicitation process must consider organizational, environmental, project, and people constraints. Knowledge is acquired through reading, listening, asking questions, and observation.
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2. • Software Measurement: A measurement is a manifestation of the
size, quantity, amount, or dimension of a particular attribute of a
product or process. Software measurement is a titrate impute of a
characteristic of a software product or the software process. It is an
authority within software engineering.
3. • Software Measurement Principles:
• The software measurement process can be characterized by five activities-
• Formulation: The derivation of software measures and metrics appropriate
for the representation of the software that is being considered.
• Collection: The mechanism used to accumulate data required to derive the
formulated metrics.
• Analysis: The computation of metrics and the application of mathematical
tools.
• Interpretation: The evaluation of metrics resulting in insight into the
quality of the representation.
• Feedback: Recommendation derived from the interpretation of product
metrics transmitted to the software team.
4. Need for Software Measurement:
• Software is measured to:
• Create the quality of the current product or process.
• Anticipate future qualities of the product or process.
• Enhance the quality of a product or process.
• Regulate the state of the project in relation to budget and schedule.
• Classification of Software Measurement:
• There are 2 types of software measurement:
• Direct Measurement: In direct measurement, the product, process, or
thing is measured directly using a standard scale.
• Indirect Measurement: In indirect measurement, the quantity or quality to
be measured is measured using related parameters i.e. by use of reference.
5. Metrics:
• A metric is a measurement of the level at which any impute belongs
to a system product or process.
• Software metrics will be useful only if they are characterized
effectively and validated so that their worth is proven. There are 4
functions related to software metrics:
• Planning
• Organizing
• Controlling
• Improving
6. Characteristics of software Metrics:
•
• Quantitative: Metrics must possess quantitative nature. It means metrics
can be expressed in values.
• Understandable: Metric computation should be easily understood, and the
method of computing metrics should be clearly defined.
• Applicability: Metrics should be applicable in the initial phases of the
development of the software.
• Repeatable: The metric values should be the same when measured
repeatedly and consistent in nature.
• Economical: The computation of metrics should be economical.
• Language Independent: Metrics should not depend on any programming
language.
7. Classification of Software Metrics:
• There are 3 types of software metrics:
• Product Metrics: Product metrics are used to evaluate the state of
the product, tracing risks and undercover prospective problem areas.
The ability of the team to control quality is evaluated.
• Process Metrics: Process metrics pay particular attention to
enhancing the long-term process of the team or organization.
• Project Metrics: The project matrix describes the project
characteristic and execution process.
8. Lines of Code (LOC) in Software Engineering
• A line of code (LOC) is any line of text in a code that is not a comment
or blank line, and also header lines, in any case of the number of
statements or fragments of statements on the line. LOC clearly
consists of all lines containing the declaration of any variable, and
executable and non-executable statements. As Lines of Code (LOC)
only counts the volume of code, you can only use it to compare or
estimate projects that use the same language and are coded using the
same coding standards.
9. Line of Code
• Features :
• Variations such as “source lines of code”, are used to set out a codebase.
• LOC is frequently used in some kinds of arguments.
• They are used in assessing a project’s performance or efficiency.
• Advantages :
• Most used metric in cost estimation.
• Its alternates have many problems as compared to this metric.
• It is very easy in estimating the efforts.
• Disadvantages :
• Very difficult to estimate the LOC of the final program from the problem specification.
• It correlates poorly with quality and efficiency of code.
• It doesn’t consider complexity.
10.
11. Function Point (FP) Metric
• The function point metric is used to measure
• the functionality delivered by the system
• estimate the effort
• predict the number of errors
• estimate the number of components in the system.
Function point is derived by using a relationship between the complexity of
software and the information domain value. Information domain values
used in function point include
the number of external inputs
external outputs
external inquires
internal logical files
the number of external interface files.
12. Counting Function Point (FP):
Step-1:F = 14 * scale
Scale varies from 0 to 5 according to character of Complexity Adjustment Factor (CAF).
Below table shows scale:
0 - No Influence 1 - Incidental 2 - Moderate 3 - Average 4 - Significant 5 - Essential
Step-2: Calculate Complexity Adjustment Factor (CAF).CAF = 0.65 + ( 0.01 * F )
Function Units Low Avg High
EI 3 4 6
EO 4 5 7
EQ 3 4 6
ILF 7 10 15
EIF 5 7 10
ILF
EIF
•Step-3: Calculate Unadjusted Function Point (UFP).
TABLE (Required)
Multiply each individual function point to corresponding values in TABLE.
13. Multiply each individual function point to corresponding values in TABLE.
Step-4: Calculate Function Point.
FP = UFP * CAF
Example:
Given the following values, compute function point when all complexity adjustment factor (CAF) and
weighting factors are average.
User Input = 50 User Output = 40 User Inquiries = 35 User Files = 6 External Interface = 4
Explanation:
•Step-1: As complexity adjustment factor is average (given in question), hence
•scale = 3.
• F = 14 * 3 = 42
•Step-2:CAF = 0.65 + ( 0.01 * 42 ) = 1.07
•Step-3: As weighting factors are also average (given in question) hence
•we will multiply each individual function point to corresponding values in TABLE.
•UFP = (50*4) + (40*5) + (35*4) + (6*10) + (4*7) = 628
Step-4:Function Point = 628 * 1.07 = 671.96
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