The COCOMO model is a widely used software cost estimation model that predicts development effort and schedule based on project attributes. It includes basic, intermediate, and detailed models of increasing complexity. The intermediate model estimates effort as a function of source lines of code and cost drivers. The detailed model further incorporates the impact of cost drivers on development phases. COCOMO 2 expands on this with application composition, early design, reuse, and post-architecture models for different project stages.
The document discusses staffing level estimation over the course of a software development project. It describes how the number of personnel needed varies at different stages: a small group is needed for planning and analysis, a larger group for architectural design, and the largest number for implementation and system testing. It also references models like the Rayleigh curve and Putnam's interpretation that estimate personnel levels over time. Tables show estimates for the distribution of effort, schedule, and personnel across activities for different project sizes. The key idea is that staffing requirements fluctuate throughout the software life cycle, with peaks during implementation and testing phases.
The document discusses organization and team structures for software development organizations. It explains the differences between functional and project formats. The functional format divides teams by development phase (e.g. requirements, design), while the project format assigns teams to a single project. The document notes advantages of the functional format include specialization, documentation, and handling staff turnover. However, it is not suitable for small organizations with few projects. The document also describes common team structures like chief programmer, democratic, and mixed control models.
The document discusses various aspects of software project management including project planning activities like estimation, scheduling, staffing, and risk handling. It describes different project organization structures like functional organization and project organization. It also discusses different team structures like chief programmer teams, democratic teams, and mixed teams. The document emphasizes the importance of careful project planning and producing a software project management plan document. It also discusses considerations for staffing a project team and attributes of a good software engineer.
The document describes the waterfall model of software development. It begins by listing the presenters and defining sequential and incremental software development models. It then discusses the waterfall model in more detail, describing it as a linear sequential process where each phase must be completed before the next begins. The document outlines the history, use cases, diagram, phases and advantages/disadvantages of the waterfall model.
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.
The COCOMO model is a widely used software cost estimation model developed by Barry Boehm in 1981. It predicts effort, schedule, and staffing needs based on project size and characteristics. The Basic COCOMO model uses three development modes (Organic, Semidetached, Embedded) and a simple formula to estimate effort and schedule based on thousands of delivered source instructions. However, its accuracy is limited as it does not account for various project attributes known to influence costs. Function Point Analysis is an alternative size measurement that counts different types of system functions and complexity factors to estimate effort and cost.
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.
This document discusses several software cost estimation techniques:
1. Top-down and bottom-up approaches - Top-down estimates system-level costs while bottom-up estimates costs of each module and combines them.
2. Expert judgment - Widely used technique where experts estimate costs based on past similar projects. It utilizes experience but can be biased.
3. Delphi estimation - Estimators anonymously provide estimates in rounds to reach consensus without group dynamics influencing individuals.
4. Work breakdown structure - Hierarchical breakdown of either the product components or work activities to aid bottom-up estimation.
The document discusses staffing level estimation over the course of a software development project. It describes how the number of personnel needed varies at different stages: a small group is needed for planning and analysis, a larger group for architectural design, and the largest number for implementation and system testing. It also references models like the Rayleigh curve and Putnam's interpretation that estimate personnel levels over time. Tables show estimates for the distribution of effort, schedule, and personnel across activities for different project sizes. The key idea is that staffing requirements fluctuate throughout the software life cycle, with peaks during implementation and testing phases.
The document discusses organization and team structures for software development organizations. It explains the differences between functional and project formats. The functional format divides teams by development phase (e.g. requirements, design), while the project format assigns teams to a single project. The document notes advantages of the functional format include specialization, documentation, and handling staff turnover. However, it is not suitable for small organizations with few projects. The document also describes common team structures like chief programmer, democratic, and mixed control models.
The document discusses various aspects of software project management including project planning activities like estimation, scheduling, staffing, and risk handling. It describes different project organization structures like functional organization and project organization. It also discusses different team structures like chief programmer teams, democratic teams, and mixed teams. The document emphasizes the importance of careful project planning and producing a software project management plan document. It also discusses considerations for staffing a project team and attributes of a good software engineer.
The document describes the waterfall model of software development. It begins by listing the presenters and defining sequential and incremental software development models. It then discusses the waterfall model in more detail, describing it as a linear sequential process where each phase must be completed before the next begins. The document outlines the history, use cases, diagram, phases and advantages/disadvantages of the waterfall model.
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.
The COCOMO model is a widely used software cost estimation model developed by Barry Boehm in 1981. It predicts effort, schedule, and staffing needs based on project size and characteristics. The Basic COCOMO model uses three development modes (Organic, Semidetached, Embedded) and a simple formula to estimate effort and schedule based on thousands of delivered source instructions. However, its accuracy is limited as it does not account for various project attributes known to influence costs. Function Point Analysis is an alternative size measurement that counts different types of system functions and complexity factors to estimate effort and cost.
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.
This document discusses several software cost estimation techniques:
1. Top-down and bottom-up approaches - Top-down estimates system-level costs while bottom-up estimates costs of each module and combines them.
2. Expert judgment - Widely used technique where experts estimate costs based on past similar projects. It utilizes experience but can be biased.
3. Delphi estimation - Estimators anonymously provide estimates in rounds to reach consensus without group dynamics influencing individuals.
4. Work breakdown structure - Hierarchical breakdown of either the product components or work activities to aid bottom-up estimation.
This Presentation contains all the topics in design concept of software engineering. This is much more helpful in designing new product. You have to consider some of the design concepts that are given in the ppt
The document discusses the prototype model in software development. It defines a prototype model as building a working prototype of the system before full development to allow users to evaluate proposals. The key steps are requirements analysis, quick design, building the prototype, getting customer evaluation and feedback, and refining the prototype iteratively until the user is satisfied. Prototype models have advantages like early assessment, clarifying requirements, and ensuring user requirements are met. However, they can also be time-consuming and expensive if multiple prototypes are needed before finding the perfect fit.
The document discusses key concepts in software engineering. It defines software engineering as applying systematic and technical approaches to develop reliable and efficient computer software. It describes various software development models including waterfall, prototyping, RAD, spiral and evolutionary models. It also discusses software engineering layers, characteristics, applications, and process models. Finally, it covers concepts like fourth generation techniques, software project management, estimation techniques, and risk management.
This document discusses agile software development methods. It outlines the agile manifesto which values individuals and interactions over processes, working software over documentation, and customer collaboration over contract negotiation. Some key agile principles include customer satisfaction, welcome changing requirements, and frequent delivery of working software. Common agile methods like extreme programming and scrum are also summarized. Advantages include improved customer satisfaction and responsiveness to change, while disadvantages include potential lack of documentation.
The document provides an introduction to software engineering and discusses key concepts such as:
1) Software is defined as a set of instructions that provide desired features, functions, and performance when executed and includes programs, data, and documentation.
2) Software engineering applies scientific knowledge and engineering principles to the development of reliable and efficient software within time and budget constraints.
3) The software development life cycle (SDLC) involves analysis, design, implementation, and documentation phases to systematically develop high quality software that meets requirements.
This document discusses common myths held by software managers, developers, and customers. It describes myths such as believing formal standards and procedures are sufficient, thinking new hardware means high quality development, adding people to late projects will help catch up, and outsourcing means relaxing oversight. Realities discussed include standards not being used effectively, tools being more important than hardware, adding people making projects later, and needing management and control of outsourced projects. Developer myths like thinking the job is done once code runs and quality can't be assessed until code runs are addressed. The document emphasizes the importance of requirements, documentation, quality processes, and addressing change impacts.
The document discusses the spiral model of software development. The spiral model is an iterative approach that combines prototyping and aspects of the waterfall model. It was defined by Barry Boehm in 1988 as a way to address risks through iterative evaluation and improvement of prototypes. The spiral model is best for medium to high risk projects where requirements are complex or expected to change. It involves evaluating prototypes, defining new prototypes based on learnings, and repeating this process until the final product is delivered.
Introduction to Software Project ManagementReetesh Gupta
This document provides an introduction to software project management. It defines what a project and software project management are, and discusses the key characteristics and phases of projects. Software project management aims to deliver software on time, within budget and meeting requirements. It also discusses challenges that can occur in software projects related to people, processes, products and technology. Effective project management focuses on planning, organizing, monitoring and controlling the project work.
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 iterative model breaks a project into small modules that can be delivered incrementally. A working version is produced in the first module, with each subsequent release adding additional functionality until the full system is complete. It allows for quick releases during development and makes it easier to develop and test in smaller iterations while incorporating customer feedback at each stage. However, it requires more resources than traditional models and skilled management to avoid increased costs over time.
Rapid application development (RAD) aims to develop software quickly through a model with phases like business modeling, data modeling, process modeling, application generation, and testing. Business modeling defines information flow. Data modeling refines information into entities and attributes. Process modeling transforms data objects to support business functions. Automated tools help build the software. Testing reduces risk through component reuse and interface exercises. RAD requires tools like case tools, data dictionaries, storyboards, and risk registers. Advantages include quick reviews, isolation of problems, and flexibility, while disadvantages are lack of planning and need for skilled developers.
Evolutionary models are iterative and incremental software development approaches that combine iterative and incremental processes. There are two main types: prototyping and spiral models. The prototyping model develops prototypes that are tested and refined based on customer feedback until requirements are met, while the spiral model proceeds through multiple loops or phases of planning, risk analysis, engineering, and evaluation. Both approaches allow requirements to evolve through development and support risk handling.
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.
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 constructive cost model (COCOMO) was developed by Barry Boehm in the late 1970s to estimate effort, cost, and schedule for software projects. COCOMO includes two versions - COCOMO I for smaller projects under 300 KLOC, and COCOMO II for larger projects. It models projects as organic, semi-detached, or embedded based on factors like team size, developer experience, environment familiarity, and innovation level. Equations are provided to estimate effort, development time, staff size, productivity, and software metrics based on project size and model type.
Software development process models
Rapid Application Development (RAD) Model
Evolutionary Process Models
Spiral Model
THE FORMAL METHODS MODEL
Specialized Process Models
The Concurrent Development Model
This document discusses various techniques for estimating effort for software projects. It describes common challenges with software estimation like subjective nature and changing requirements. It then explains different estimation techniques like algorithmic models, expert judgment, analogy, top-down and bottom-up approaches. Specifically, it outlines the function point analysis technique and COCOMO model for estimating effort based on source lines of code and complexity factors. Finally, it lists some typical rules of thumb for software estimation from Capers Jones.
UML (Unified Modeling Language) is a diagramming language used for object-oriented programming. It can be used to describe the organization, execution, use, and deployment of a program. Design patterns describe common solutions to programming problems and always use UML diagrams. This document focuses on class diagrams, which show classes, interfaces, and their relationships. It provides examples of how to depict classes with variables and methods, and relationships between classes like inheritance.
The document discusses software project planning and estimation. It covers topics like why planning is important, project planning purpose and context, estimating resources, and software estimation methods like COCOMO. COCOMO models like basic, intermediate and detailed COCOMO are explained. The document also provides an example of using the basic COCOMO model to estimate effort and development time for a project of size 400k LOC across organic, semidetached and embedded modes.
The document summarizes the COCOMO model for estimating software development costs and effort. It discusses the three forms of COCOMO - basic, intermediate, and detailed. The basic model uses effort multipliers and loc to estimate effort and schedule. The intermediate model adds 15 cost drivers. The detailed model further adds a three-level product hierarchy and phase-sensitive effort multipliers to provide more granular estimates. Examples are provided to illustrate effort and schedule estimates for different project modes and sizes using the basic and intermediate COCOMO models.
This Presentation contains all the topics in design concept of software engineering. This is much more helpful in designing new product. You have to consider some of the design concepts that are given in the ppt
The document discusses the prototype model in software development. It defines a prototype model as building a working prototype of the system before full development to allow users to evaluate proposals. The key steps are requirements analysis, quick design, building the prototype, getting customer evaluation and feedback, and refining the prototype iteratively until the user is satisfied. Prototype models have advantages like early assessment, clarifying requirements, and ensuring user requirements are met. However, they can also be time-consuming and expensive if multiple prototypes are needed before finding the perfect fit.
The document discusses key concepts in software engineering. It defines software engineering as applying systematic and technical approaches to develop reliable and efficient computer software. It describes various software development models including waterfall, prototyping, RAD, spiral and evolutionary models. It also discusses software engineering layers, characteristics, applications, and process models. Finally, it covers concepts like fourth generation techniques, software project management, estimation techniques, and risk management.
This document discusses agile software development methods. It outlines the agile manifesto which values individuals and interactions over processes, working software over documentation, and customer collaboration over contract negotiation. Some key agile principles include customer satisfaction, welcome changing requirements, and frequent delivery of working software. Common agile methods like extreme programming and scrum are also summarized. Advantages include improved customer satisfaction and responsiveness to change, while disadvantages include potential lack of documentation.
The document provides an introduction to software engineering and discusses key concepts such as:
1) Software is defined as a set of instructions that provide desired features, functions, and performance when executed and includes programs, data, and documentation.
2) Software engineering applies scientific knowledge and engineering principles to the development of reliable and efficient software within time and budget constraints.
3) The software development life cycle (SDLC) involves analysis, design, implementation, and documentation phases to systematically develop high quality software that meets requirements.
This document discusses common myths held by software managers, developers, and customers. It describes myths such as believing formal standards and procedures are sufficient, thinking new hardware means high quality development, adding people to late projects will help catch up, and outsourcing means relaxing oversight. Realities discussed include standards not being used effectively, tools being more important than hardware, adding people making projects later, and needing management and control of outsourced projects. Developer myths like thinking the job is done once code runs and quality can't be assessed until code runs are addressed. The document emphasizes the importance of requirements, documentation, quality processes, and addressing change impacts.
The document discusses the spiral model of software development. The spiral model is an iterative approach that combines prototyping and aspects of the waterfall model. It was defined by Barry Boehm in 1988 as a way to address risks through iterative evaluation and improvement of prototypes. The spiral model is best for medium to high risk projects where requirements are complex or expected to change. It involves evaluating prototypes, defining new prototypes based on learnings, and repeating this process until the final product is delivered.
Introduction to Software Project ManagementReetesh Gupta
This document provides an introduction to software project management. It defines what a project and software project management are, and discusses the key characteristics and phases of projects. Software project management aims to deliver software on time, within budget and meeting requirements. It also discusses challenges that can occur in software projects related to people, processes, products and technology. Effective project management focuses on planning, organizing, monitoring and controlling the project work.
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 iterative model breaks a project into small modules that can be delivered incrementally. A working version is produced in the first module, with each subsequent release adding additional functionality until the full system is complete. It allows for quick releases during development and makes it easier to develop and test in smaller iterations while incorporating customer feedback at each stage. However, it requires more resources than traditional models and skilled management to avoid increased costs over time.
Rapid application development (RAD) aims to develop software quickly through a model with phases like business modeling, data modeling, process modeling, application generation, and testing. Business modeling defines information flow. Data modeling refines information into entities and attributes. Process modeling transforms data objects to support business functions. Automated tools help build the software. Testing reduces risk through component reuse and interface exercises. RAD requires tools like case tools, data dictionaries, storyboards, and risk registers. Advantages include quick reviews, isolation of problems, and flexibility, while disadvantages are lack of planning and need for skilled developers.
Evolutionary models are iterative and incremental software development approaches that combine iterative and incremental processes. There are two main types: prototyping and spiral models. The prototyping model develops prototypes that are tested and refined based on customer feedback until requirements are met, while the spiral model proceeds through multiple loops or phases of planning, risk analysis, engineering, and evaluation. Both approaches allow requirements to evolve through development and support risk handling.
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.
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 constructive cost model (COCOMO) was developed by Barry Boehm in the late 1970s to estimate effort, cost, and schedule for software projects. COCOMO includes two versions - COCOMO I for smaller projects under 300 KLOC, and COCOMO II for larger projects. It models projects as organic, semi-detached, or embedded based on factors like team size, developer experience, environment familiarity, and innovation level. Equations are provided to estimate effort, development time, staff size, productivity, and software metrics based on project size and model type.
Software development process models
Rapid Application Development (RAD) Model
Evolutionary Process Models
Spiral Model
THE FORMAL METHODS MODEL
Specialized Process Models
The Concurrent Development Model
This document discusses various techniques for estimating effort for software projects. It describes common challenges with software estimation like subjective nature and changing requirements. It then explains different estimation techniques like algorithmic models, expert judgment, analogy, top-down and bottom-up approaches. Specifically, it outlines the function point analysis technique and COCOMO model for estimating effort based on source lines of code and complexity factors. Finally, it lists some typical rules of thumb for software estimation from Capers Jones.
UML (Unified Modeling Language) is a diagramming language used for object-oriented programming. It can be used to describe the organization, execution, use, and deployment of a program. Design patterns describe common solutions to programming problems and always use UML diagrams. This document focuses on class diagrams, which show classes, interfaces, and their relationships. It provides examples of how to depict classes with variables and methods, and relationships between classes like inheritance.
The document discusses software project planning and estimation. It covers topics like why planning is important, project planning purpose and context, estimating resources, and software estimation methods like COCOMO. COCOMO models like basic, intermediate and detailed COCOMO are explained. The document also provides an example of using the basic COCOMO model to estimate effort and development time for a project of size 400k LOC across organic, semidetached and embedded modes.
The document summarizes the COCOMO model for estimating software development costs and effort. It discusses the three forms of COCOMO - basic, intermediate, and detailed. The basic model uses effort multipliers and loc to estimate effort and schedule. The intermediate model adds 15 cost drivers. The detailed model further adds a three-level product hierarchy and phase-sensitive effort multipliers to provide more granular estimates. Examples are provided to illustrate effort and schedule estimates for different project modes and sizes using the basic and intermediate COCOMO models.
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.
The document discusses Putnam's resource allocation model and the COCOMO cost estimation models, including:
1) Putnam derived the Rayleigh-Norden curve, which relates software development effort, time, and lines of code based on the technology constant Ck and project environment.
2) The Basic COCOMO model estimates effort, time, staff size, and productivity for projects based on size (KLOC) and type (organic, semidetached, embedded).
3) The Intermediate COCOMO model improves accuracy by including 15 cost drivers that adjust estimates based in development environment factors.
4) The Detailed COCOMO model captures more project characteristics to construct software estimates. COCOMO-II
This document discusses software project management and estimation techniques. It covers:
- Project management involves planning, monitoring, and controlling people and processes.
- Estimation approaches include decomposition techniques and empirical models like COCOMO I & II.
- COCOMO I & II models estimate effort based on source lines of code and cost drivers. They include basic, intermediate, and detailed models.
- Other estimation techniques discussed include function point analysis and problem-based estimation.
This document discusses various software metrics that can be used for software estimation, quality assurance, and maintenance. It describes black box metrics like function points and COCOMO, which focus on program functionality without examining internal structure. It also covers white box metrics, including lines of code, Halstead's software science, and McCabe's cyclomatic complexity, which measure internal program properties. Finally, it discusses using metrics like change rates and effort adjustment factors to estimate software maintenance costs.
This document discusses project estimation and the Constructive Cost Model (COCOMO) for estimating software development costs and schedules. It explains that inaccurate estimates often lead to cost overruns and project failures. Several estimation methods are described like expert judgment, analogy models, and algorithmic models. The COCOMO model uses variables like project size, mode (organic, semidetached, embedded), and effort adjustment factors to estimate effort (in person-months), development time, and staffing needs. The basic, intermediate, and detailed COCOMO models are explained along with the equations used for effort and schedule estimates. Factors that impact productivity like application experience, process quality, and technology are also summarized.
The COCOMO model is a software cost estimation model that estimates development effort based on lines of code. There are three modes - organic, semi-detached, and embedded - that represent increasing complexity. The basic COCOMO model uses lines of code and coefficients to estimate effort and schedule. The intermediate COCOMO model refines estimates using 15 cost drivers related to product, computer, personnel, and project attributes. The detailed COCOMO model further divides the project into phases and modules to provide more granular estimates.
This document discusses software cost estimation. It describes the objectives of software cost estimation and the fundamental questions that must be answered. It explains different techniques for software cost estimation including algorithmic cost modeling, expert judgement, and estimation by analogy. A key part of the document focuses on the COCOMO model, including the different levels of COCOMO 2 for estimation. It also discusses using cost estimation for project planning and management.
COCOMO is a widely used software cost estimation model developed by Barry Boehm in 1981. It estimates effort, schedule, and staffing needs based on program size and complexity factors. COCOMO defines three project types - organic, semidetached, and embedded - and three model forms of increasing accuracy and detail: basic, intermediate, and detailed. The intermediate model refines the basic model's estimates using 15 cost drivers, while the detailed model divides projects into modules and assesses each development phase separately.
The document provides information on cost estimation models for software projects. It discusses static single variable and multi-variable models that estimate cost based on lines of code. The COCOMO model is also described, including the basic, intermediate, and complete versions. The intermediate COCOMO model uses 15 cost drivers to adjust the nominal effort based on project attributes. Formulas are provided for estimating effort, duration, and staff requirements for different COCOMO modes and models. Examples are given to demonstrate effort and duration calculations for single and multi-variable models.
The document discusses techniques for estimating project metrics like effort, cost, and duration for software projects. It describes the COCOMO model which categorizes projects as organic, semidetached, or embedded based on characteristics. The basic COCOMO model estimates effort and time using project size, while the intermediate model refines estimates using cost drivers. The complete COCOMO model treats a project as multiple components. Halstead's metrics also estimate effort using program operands and operators. The document provides an example of estimating metrics for a library information system project using COCOMO.
The document discusses software project management responsibilities and activities. It covers project planning, estimation techniques, scheduling, and monitoring. Project managers are responsible for overall project success through tasks like planning, cost estimation, risk management, and client interfacing. Accurate project planning requires estimating project size, cost, duration, and effort. Techniques include expert judgment, Delphi, and mathematical models. The COCOMO model specifically estimates effort and time based on project size. Project managers use techniques like sliding window planning to address risks from inaccurate initial estimates.
This document discusses software cost estimation. It introduces metrics for assessing software productivity and techniques for estimating software costs, including algorithmic modeling. A key technique is the COCOMO model, which relates software size to project costs based on historical data. The document also covers how to estimate project duration and staffing needs.
The document provides information on cost estimation techniques for software projects. It discusses how complexity, size, efforts, and time relate to each other in cost models. Size is typically measured in thousands of lines of code (KSLOC). Efforts are estimated by multiplying KSLOC by a productivity factor. For larger projects, a size penalty factor is included. Function point analysis is an alternative to estimating directly from KSLOC by evaluating inputs, outputs, interfaces, and files.
This document discusses project estimation techniques. It outlines several techniques for estimating project parameters like activity resources, durations, and costs. These include pure expert judgement, historical data, function points, story points, and COCOMO (Constructive Cost Model). It also discusses the importance of software risk management and how anticipating and addressing risks can help complete projects on time and on budget by reducing rework. Establishing strong customer relationships and using tools and metrics can help manage project risks.
The COCOMO model is a family of software cost estimation models developed by Barry Boehm. It includes COCOMO I from 1981 and COCOMO II from 2000. The models take source lines of code (SLOC) as input and provide estimates of effort (person-months), development time, and staffing needed. Later models added more parameters to improve accuracy. COCOMO I has basic, intermediate, and detailed models. COCOMO II models include application composition, early design, and post-architecture models. Both use effort adjustment factors/multipliers to account for attributes like requirements, team experience, and schedule.
The document provides information on software engineering and the software development process. It defines software and discusses its characteristics, categories, and applications. It then describes software engineering, the software process, process models like waterfall and incremental/iterative models. It also discusses software process assessment using CMM levels and objectives of the software process. Overall, the document provides a comprehensive overview of key concepts relating to software, software engineering, and the software development process.
A distributed system is a collection of independent computers that appears as a single coherent system to its users. It allows sharing of resources and workload across networked computers. Key characteristics include multiple autonomous components, lack of shared memory, and message-based communication. The World Wide Web is a large-scale distributed system that allows sharing of documents, files, and other resources across the internet through web servers and browsers. It faces challenges like heterogeneity, security, scalability, and fault tolerance.
This document provides an overview of human psychology and cognition, including:
- The human senses of vision, hearing, touch, and movement and how we process sensory information.
- Memory systems including sensory memory, short-term memory, and long-term memory.
- Thinking processes like reasoning, problem solving, and potential sources of errors.
- The influence of emotion on human capabilities and performance.
- Individual differences in abilities and how context can affect cognition.
- Applications of psychological principles to the design of interactive systems.
The document discusses XPath, which is a language for finding information in an XML document. It defines XPath syntax using path expressions to select nodes. It describes XPath terminology like nodes, relationships between nodes, and functions. Examples are provided to demonstrate XPath expressions for selecting elements, attributes, and filtering nodes. Predicates are also described for finding specific nodes or values.
The document discusses XML namespaces and how they provide a method to avoid element name conflicts. It explains that namespaces allow XML parsers to distinguish between identically named elements that have different meanings. Namespaces are declared either with a default declaration that specifies a namespace for all child elements, or with an explicit declaration that associates a prefix with a namespace. Child elements inherit their parent's namespace by default but can also specify a different namespace.
The document discusses XML document structure and XML schema. It provides information on the key components of an XML document including the XML declaration, document type declaration, element data, attribute data, and character data. It then describes XML schema in detail, explaining that it defines the structure of an XML document. Key aspects of XML schema covered include elements, attributes, simple vs complex types, and restrictions.
The document discusses XML document structure and validation. It introduces well-formed and valid XML documents, and the DTD and XML Schema used to define the structure and elements of valid XML documents. It provides examples of DTDs defining the elements and attributes of sample XML documents.
The document discusses the objectives and syllabus of an IT course on Service Oriented Architecture, including learning XML fundamentals, building XML-based applications, understanding SOA principles and web services technologies, and building SOA-based applications; it provides the textbook and reference book details and outlines the topics to be covered in each unit such as XML document structure, building XML applications, SOA, and web services.
This document discusses the structure and components of an XML document. It explains that an XML document consists of elements, attributes, comments, processing instructions, and a document type declaration. It describes each of these components in detail, including their purpose and general syntax. The document type declaration identifies the document and can reference an internal or external DTD that defines the valid elements and attributes.
The document discusses UML deployment diagrams which visualize the physical topology of a system where software artifacts are deployed. Deployment diagrams show nodes, which can be device or execution environment nodes, and artifacts deployed on the nodes. Communication paths represent connections between deployment targets that allow exchange of signals and messages. Deployments show the allocation of artifacts to deployment targets using dependencies labeled with "deploy". An example diagram depicts the deployment of a web application across load balanced servers.
The document discusses UML component diagrams. It defines a component as a modular, deployable, and replaceable part of a system that encapsulates implementation and exposes interfaces. It describes how component diagrams show the various components in a system and their dependencies. It outlines the purpose of component diagrams as visualizing components, constructing executables through forward and reverse engineering, and describing component organization and relationships.
The document discusses UML package diagrams and their notations and purposes. It describes how package diagrams can be used to logically modularize complex diagrams and organize source code. It explains how packages, elements, dependencies, imports, visibility, qualified names, and merges are represented in package diagrams.
The document discusses UML diagrams including state diagrams and activity diagrams. For state diagrams, it describes how they model the dynamic aspects and states of an object over time through states, transitions, and events. The key elements of a state diagram like states, transitions, and events are defined. It also provides an example state diagram. For activity diagrams, it describes how they model the flow of activities in a system through activity nodes and edges. The basic components of an activity diagram like activities, forks, joins, and decisions are outlined. It concludes with the main uses of activity diagrams to model workflows and business requirements.
The document discusses UML interaction diagrams, which model how objects collaborate and interact to complete use case tasks. There are two main types: sequence diagrams, which show the time-based sequence of messages between objects, and collaboration diagrams, which emphasize the structural organization and associations between objects. Sequence diagrams use lifelines and messages to represent an object's lifespan and the messages it sends/receives. Collaboration diagrams show objects and their relationships through associations and numbered messages. Both can be used to design and understand complex interactions in object-oriented systems.
This document discusses UML class diagrams. It begins by explaining that a class diagram is a static structure diagram that describes the structure of a system by showing classes, interfaces, associations, collaborations and constraints. It then discusses key aspects of class diagrams including notations used to represent classes and relationships, common relationships like association, aggregation, composition, generalization and realization. It provides examples to illustrate these concepts.
The document discusses various UML diagrams including use case diagrams, describing use cases as interactions between actors and systems to achieve goals, use case elements such as actors, scenarios, and relationships including generalization, extension, and inclusion. It provides examples of use case diagrams and templates for an ATM system and a school management system.
Here are the steps to solve this problem:
1) %d - Print integer in decimal format. 72 in decimal is 72.
2) %o - Print integer in octal format. 72 in octal is 114.
3) %x - Print integer in hexadecimal format. 72 in hexadecimal is 48.
So the output of the program will be:
721148
The document introduces UML (Unified Modeling Language) diagrams which are used to visualize, specify, construct and document software systems and business processes. It discusses the main UML diagram types - use case diagrams, class diagrams, interaction diagrams, state diagrams and activity diagrams. It also explains that UML can be used at different levels of detail from informal sketches to detailed blueprints and executable specifications. UML supports object-oriented software design and can model concepts, software specifications or implementations depending on the perspective.
The document discusses different types of UML diagrams used to model software systems from various perspectives. It introduces UML and its purposes of specifying, constructing and documenting system artifacts. There are two main categories of UML diagrams - structure diagrams that show system structure and relationships, and behavioral diagrams that show dynamic object interactions. Specific structure diagrams covered are class, package, component and deployment diagrams. Behavioral diagrams covered include use case, activity, state, sequence and collaboration diagrams.
This document provides an overview of the CS6502 Object Oriented Analysis and Design course. The course covers UML design diagrams, design patterns, case studies, applying design patterns, and coding and testing. It discusses the objectives of learning OOAD skills, UML, mapping design to code, and testing techniques. Textbooks and reference materials are also listed. The syllabus outlines five units covering UML diagrams, design patterns, a case study, applying patterns, and coding and testing.
Data Communication and Computer Networks Management System Project Report.pdfKamal Acharya
Networking is a telecommunications network that allows computers to exchange data. In
computer networks, networked computing devices pass data to each other along data
connections. Data is transferred in the form of packets. The connections between nodes are
established using either cable media or wireless media.
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Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
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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Online train ticket booking system project.pdfKamal Acharya
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
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
Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
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Cocomo model
1. COCOMO MODEL
(Cost Constructive MOdel)
Most widely used software estimation
model.
COCOMO predicts the efforts and
schedule of a software product.
2. SEG3300 A&B W2004 R.L. Probert 2
COCOMO Models
• COCOMO is defined in terms of three different
models:
– the Basic model,
– the Intermediate model, and
– the Detailed model.
• The more complex models account for more
factors that influence software projects, and
make more accurate estimates.
3. SEG3300 A&B W2004 R.L. Probert 3
The Development mode
• the most important factors contributing to a
project's duration and cost is the
Development Mode
• Organic Mode: The project is developed in a familiar,
stable environment, and the product is similar to
previously developed products. The product is
relatively small, and requires little innovation.
• Semidetached Mode: The project's characteristics are
intermediate between Organic and Embedded.
4. SEG3300 A&B W2004 R.L. Probert 4
The Development mode
• the most important factors contributing to a
project's duration and cost is the
Development Mode:
• Embedded Mode: The project is characterized by tight,
inflexible constraints and interface requirements. An
embedded mode project will require a great deal of
innovation.
5. TCS2411 Software Engineering 5
Basic COCOMO model
• Computes software development effort (and
cost) as function of program size expressed in
estimated lines of code
• Model:
Category ab bb cb db
Organic 2.4 1.05 2.5 0.38
Semi-detached 3.0 1.12 2.5 0.35
Embedded 3.6 1.20 2.5 0.32
6. TCS2411 Software Engineering 6
Basic COCOMO Equations
where
• E is effort in person-months
• D is development time in months
• kLOC is estimated number of lines of code
b
b
d
b
b
b
EcD
kLOCaE
8. Merits
• Good for quick,early,rough order of estimates
Limitations:
• Accuracy is limited
• Does not consider certain factors(H/W
constraints,personal quality,experience,tools)
9. Example
• Consider a software project using semi-
detached mode with 30000 lines of code.We
will obtain estimation for this project as
follows:
• E=3.0(30)1.12
=135 person-month
12. TCS2411 Software Engineering 12
Intermediate COCOMO
• computes software development effort as a
function of program size and a set of “cost
drivers” that include subjective assessments of
product, hardware, personnel, and project
attributes
• Give rating to 15 attributes, from “very low” to
“extra high”, find effort multipllier (from table)
and product of all effort multipliers gives an
effort adjustment factor (EAF)
14. TCS2411 Software Engineering 14
Cost Driver Attributes (Continued)
• Personnel attributes
– Analyst capability, Programmer capability
– Applications experience
– Virtual machine experience
– Programming language experience
• Project attributes
– Use of modern programming practices
– Use of software tools
– Required development schedule
15. TCS2411 Software Engineering 15
Intermediate COCOMO Equation
• where
• E is effort in person-months,
• kLOC is estimated number of lines of code
Category ai bi
Organic 3.2 1.05
Semi-detached 3.0 1.12
Embedded 2.8 1.20
EAFkLOCaE ib
i
16. Merits
• Can be applied to almost entire software for
easy and rough cost estimation
• Can be applied at the s/w product component
level
Limitations:
Many components difficult to estimate
17. TCS2411 Software Engineering 17
Advanced COCOMO
• Incorporates all characteristics of intermediate
COCOMOwith an assessment of the cost
driver’s impact on each step of software
engineering process
18. COCOMO 2 models
• COCOMO 2 incorporates a range of sub-models that produce
increasingly detailed software estimates.
• The sub-models in COCOMO 2 are:
– Application composition model. Used when software is composed
from existing parts.
– Early design model. Used when requirements are available but design
has not yet started.
– Reuse model. Used to compute the effort of integrating reusable
components.
– Post-architecture model. Used once the system architecture has been
designed and more information about the system is available.
20. Application composition model
• Supports prototyping projects and projects where there is
extensive reuse.
• Based on standard estimates of developer productivity in
application (object) points/month.
• Takes CASE tool use into account.
• Formula is
– PM = ( NAP (1 - %reuse/100 ) ) / PROD
– PM is the effort in person-months, NAP is the number of application
points and PROD is the productivity.
21. Object point productivity
DeveloperÕs experience
and capability
Very low Low Nominal High Very high
ICASE maturity and
capability
Very low Low Nominal High Very high
PROD (NOP/month) 4 7 13 25 50
22. Early design model
• Estimates can be made after the requirements
have been agreed.
• Based on a standard formula for algorithmic
models
– PM = A SizeB M where
– M = PERS RCPX RUSE PDIF PREX FCIL
SCED;
– A = 2.94 in initial calibration, Size in KLOC, B varies
from 1.1 to 1.24 depending on novelty of the
project, development flexibility, risk management
approaches and the process maturity.
23. Multipliers
• Multipliers reflect the capability of the
developers, the non-functional requirements,
the familiarity with the development platform,
etc.
– RCPX - product reliability and complexity;
– RUSE - the reuse required;
– PDIF - platform difficulty;
– PREX - personnel experience;
– PERS - personnel capability;
– SCED - required schedule;
– FCIL - the team support facilities.
24. The reuse model
• Takes into account black-box code that is
reused without change and code that has to
be adapted to integrate it with new code.
• There are two versions:
– Black-box reuse where code is not modified. An
effort estimate (PM) is computed.
– White-box reuse where code is modified. A size
estimate equivalent to the number of lines of new
source code is computed. This then adjusts the
size estimate for new code.
25. Reuse model estimates 1
• For generated code:
– PM = (ASLOC * AT/100)/ATPROD
– ASLOC is the number of lines of generated code
– AT is the percentage of code automatically
generated.
– ATPROD is the productivity of engineers in
integrating this code.
26. Reuse model estimates 2
• When code has to be understood and
integrated:
– ESLOC = ASLOC * (1-AT/100) * AAM.
– ASLOC and AT as before.
– AAM is the adaptation adjustment multiplier
computed from the costs of changing the reused
code, the costs of understanding how to integrate
the code and the costs of reuse decision making.
27. Post-architecture level
• Uses the same formula as the early design model
but with 17 rather than 7 associated multipliers.
• The code size is estimated as:
– Number of lines of new code to be developed;
– Estimate of equivalent number of lines of new code
computed using the reuse model;
– An estimate of the number of lines of code that have
to be modified according to requirements changes.
28. • This depends on 5 scale factors (see next slide). Their
sum/100 is added to 1.01
• A company takes on a project in a new domain. The client has
not defined the process to be used and has not allowed time
for risk analysis. The company has a CMM level 2 rating.
– Precedenteness - new project (4)
– Development flexibility - no client involvement - Very high (1)
– Architecture/risk resolution - No risk analysis - V. Low .(5)
– Team cohesion - new team - nominal (3)
– Process maturity - some control - nominal (3)
• Scale factor is therefore 1.17.
The exponent term
29. Exponent scale factors
Precedentedness Reflects the previous experience of the organisation with this type of
project. Very low means no previous experience, Extra high means
that the organisation is completely familiar with this application
domain.
Development
flexibility
Reflects the degree of flexibility in the development process. Very
low means a prescribed process is used; Extra high means that the
client only sets general goals.
Architecture/risk
resolution
Reflects the extent of risk analysis carried out. Very low means little
analysis, Extra high means a complete a thorough risk analysis.
Team cohesion Reflects how well the development team know each other and work
together. Very low means very difficult interactions, Extra high
means an integrated and effective team with no communication
problems.
Process maturity Reflects the process maturity of the organisation. The computation
of this value depends on the CMM Maturity Questionnaire but an
estimate can be achieved by subtracting the CMM process maturity
level from 5.
31. TCS2411 Software Engineering 31
References
• “Software Engineering: A Practitioner’s
Approach” 5th Ed. by Roger S. Pressman, Mc-
Graw-Hill, 2001
• “Software Engineering” by Ian Sommerville,
Addison-Wesley, 2001
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