The document discusses software cost estimation and provides information on three main approaches: using experience and historical data, decomposition techniques, and empirical models. It describes decomposition techniques that break down a problem or process into components that can be estimated individually. Empirical models provide formulas to predict effort as a function of lines of code or function points. The document also discusses factors that influence cost like size, complexity, programmer ability, schedule, and reliability. It provides details on the COCOMO model and equations.
This document discusses using function points to estimate the cost and time required for a software project. It provides an example of estimating a program for a phone order company. The example counts the function points for the program's inputs, outputs, files and queries, then converts this to lines of code and staff hours using standard metrics. Finally, it calculates the estimated cost for the project based on typical programmer salaries. The document advocates that function point analysis provides a more accurate estimation approach than just comparing to past similar projects.
Decomposition technique In Software Engineering Bilal Hassan
The document discusses different techniques for estimating software project costs and effort, including decomposition, sizing, and function point analysis. It provides an example of estimating the lines of code and function points for a mechanical CAD software project. Estimates are developed by decomposing the problem into smaller elements and tasks, and estimating the effort required for each. The accuracy of estimates depends on properly sizing the software and having reliable past project metrics.
This document discusses software cost estimation and factors that influence productivity. It defines software cost estimation as predicting resources needed for development like effort, time and total cost. Cost components include hardware/software, travel/training, and effort costs like salaries and overheads. Productivity measures include lines of code, function points based on functionality, and object points. Factors like language, code verbosity, and system characteristics can impact productivity estimates.
Introduction to Software Cost EstimationHemanth Raj
This document provides an introduction to software cost estimation. It discusses how software cost estimation has evolved from simple programs with few parameters to complex modern applications that require estimating numerous parameters. Key parameters that influence cost estimates include project size, requirements volatility, team capabilities, and development tools and methodologies to be used. The document also describes how commercial software cost estimation tools work, including using templates based on attributes of similar past projects to generate estimates. Finally, it outlines the typical 10-step sequence used to generate software cost estimates.
This document discusses software estimation techniques. It defines estimation, targets, and commitments, distinguishing between them. It describes reasons for inaccurate estimates like unstable requirements, omitted activities, and unrealistic optimism. It discusses fundamental estimation techniques like counting and computing, and choosing techniques based on project size, development style, and stage. Throughout, it emphasizes removing uncertainty through techniques like requirements stabilization to improve estimates.
This document provides an overview of several software estimation techniques: lines of code estimation, function point estimation, three point estimation, work breakdown structure based estimation, use case based estimation, and estimation in agile projects. It discusses the basics of each technique, including counting lines of code, function points types, the three point estimation formula, how to create a work breakdown structure, and use case point estimation. Examples are provided to illustrate various techniques.
Cocomo ( cot constrictive model) and capability maturity modelPrakash Poudel
The document provides details about the COCOMO cost estimation model and its various stages and formulas. It discusses the three classes of software projects - Organic, Semi-Detached and Embedded - that COCOMO can be applied to. The stages of COCOMO are described as Basic, Intermediate and Detailed. Formulas for effort, time and people estimation are provided for each stage. An example case study is presented to demonstrate the use of COCOMO.
Wideband Delphi is a consensus-based estimation technique involving a team that creates task estimates through an iterative process. It begins with a kickoff meeting where the team generates tasks and assumptions. Members then independently estimate effort for each task. Next, an estimation session is held where the team revises estimates through discussion to reach consensus. Finally, the project manager assembles the results into a final task list and estimate report. The technique leverages group expertise and iteration to create accurate and agreed-upon estimates.
This document provides an overview of software estimation techniques. It discusses why estimation is important, the general estimation process, and factors that impact accuracy such as requirements management and experience. The document also describes different estimation methods like expert judgment, analogy-based, top-down, and bottom-up. It provides examples of estimation tools like Function Point Analysis and COCOMO II for sizing and effort estimation.
The document discusses software cost estimation and planning. It describes several models for software cost estimation including COCOMO and Putnam models. COCOMO uses staff months and lines of code to initially estimate effort which is then adjusted based on cost drivers. Putnam uses a Rayleigh curve staffing model based on volume, difficulty, and time constraints. Thorough planning is important to software projects and factors like life cycle, quality assurance, and risk management should be considered. Historical data and validated models can help produce more accurate cost and schedule estimates.
Effort estimation is the process of predicting the most realistic amount of effort (expressed in terms of person-hours or money) required to develop or maintain software based on incomplete, uncertain and noisy input.
Effort estimation is essential for many people and different departments in an organization.
This document provides an overview and introduction to software metrics. It discusses measurement concepts and why measurement is important for software engineering. It covers topics like the basics of measurement, collecting metrics data, analyzing data, and measuring internal and external attributes of software. Specific metrics discussed include size, structure, complexity, reliability, and test coverage. The document is intended to introduce readers to fundamental software metrics concepts.
The document discusses various software estimation techniques including function point analysis, three point estimation, and COCOMO. It outlines the importance of estimation for budgeting, planning, and customer satisfaction. Different techniques are suited for different project stages and sizes. Function point analysis involves counting logical files, external inputs, external outputs, external queries, and other factors to estimate effort. Three magic formulae can also help estimate project duration, staffing needs, and minimum time. Metrics like effort slippage and percentage of efforts used help evaluate estimation accuracy.
1. Software project estimation involves decomposing a project into smaller problems like major functions and activities. Estimates can be based on similar past projects, decomposition techniques, or empirical models.
2. Accurate estimates depend on properly estimating the size of the software product using techniques like lines of code, function points, or standard components. Baseline metrics from past projects are then applied to the size estimates.
3. Decomposition techniques involve estimating the effort needed for each task or function and combining them. Process-based estimation decomposes the software process into tasks while problem-based estimation decomposes the problem.
Estimating involves forecasting the time and cost to complete project deliverables. There are two main types of estimates: bottom-up estimates require more effort but rely on those familiar with the work, while top-down estimates can be made by managers without direct experience. Software cost and effort estimation is not an exact science due to many variable factors. Key parameters that affect estimates include resources, time, human skills, and cost. Common software estimation techniques include top-down and bottom-up methods such as the three-point estimation technique.
This document discusses various software project estimation methods, including bottom-up, top-down, parametric, and analogy-based estimation. It describes the COCOMO model for estimating software development effort and provides details on its constants, development effort multipliers, and the stages of analogy-based estimation. Function point models like Mark II and COSMIC are also summarized as methods of measuring software size.
This document provides an overview of software cost estimation techniques. It discusses factors that affect cost such as effort required, calendar time, and total cost of activities. It also covers parameters like hardware/software costs, travel/training costs, and effort costs. Productivity factors and techniques like algorithmic cost modeling, expert judgement, estimation by analogy, and pricing strategies are summarized. Algorithmic cost modeling uses historical data to relate software size to cost and involves factors for effort estimation.
This document presents information on cost estimation using the COCOMO model. It discusses the basic, intermediate, and detailed COCOMO models. The basic model uses effort multipliers, staff size, and productivity equations to estimate effort and schedule for projects of different modes (organic, embedded, semidetached). The intermediate model adds 15 cost drivers to improve accuracy. The detailed model incorporates three product levels, phase-sensitive effort multipliers, and effort/time fractions for each development phase.
The Wideband Delphi Method is a consensus-based software estimation technique involving multiple steps: a kickoff meeting where stakeholders generate tasks and assumptions, individual preparation where estimators provide effort estimates, an estimation session where estimates are discussed and revised, and a final review. The process aims to develop accurate estimates by leveraging group judgment and addressing uncertainties through documented assumptions.
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.
This document summarizes LOC (lines of code), function points, COCOMO I and COCOMO II cost estimation models. It defines LOC and function points for measuring software size. It then describes the Basic, Intermediate and Detailed COCOMO models, which use regression formulas to estimate software development effort based on project size and characteristics. COCOMO II models include Application Composition, Early Design and Post Architecture models. An example calculation is provided for each model.
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 document discusses using use case points (UCP) to estimate software development effort. UCP involves classifying use cases and actors based on complexity, then calculating unadjusted use case and actor weights. Technical and environmental factors are also assessed. These variables are used in an equation to determine the adjusted use case points and estimated effort in hours or weeks. The document presents this method and tools to automate it. It also compares UCP to function points and shares results from applying UCP in three industry projects, finding the estimates were close to expert assessments.
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 document discusses several popular effort estimation methodologies including function points and COCOMO. It provides examples of using function points and COCOMO I to estimate effort and schedule for a simple POWER function project estimated to be 100 lines of code. Estimates using different approaches were: 5 person days and 3 calendar days from personal experience, 7.9 person days and 7.9 calendar days from function points, and 6.7 person days and 1.5 calendar months from COCOMO I. The document notes challenges with estimation models and many professionals rely on their own experience and company data.
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.
IRJET- Analysis of Software Cost Estimation TechniquesIRJET Journal
This document analyzes and compares different software cost estimation techniques using machine learning algorithms. It uses the COCOMO and function point estimation models on NASA project datasets to test the performance of the ZeroR and M5Rules classifiers. The M5Rules classifier produced more accurate results with lower mean absolute errors and root mean squared errors compared to COCOMO, function points, and the ZeroR classifier. Therefore, the study suggests using M5Rules techniques to build models for more precise software effort 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.
COCOMO (Constructive Cost Model) is a software cost estimation model developed by Barry Boehm in 1981. It estimates the effort required to develop a software project in terms of person-months. The model has three main steps: 1) Calculate an initial effort estimate based on thousands of delivered lines of source code. 2) Determine a set of 15 factors that adjust the initial estimate based on project attributes. 3) Calculate the final effort estimate by multiplying the initial estimate by the adjustment factors. The model accounts for factors like system size, organizational experience, and project attributes to provide a cost estimate for a software project.
The document discusses the COCOMO model, which stands for Constructive Cost Model. It was developed by Barry Boehm to estimate effort, cost, and schedule for software projects. The COCOMO model has three types - basic, intermediate, and detailed - that estimate effort based on lines of code and complexity factors. The basic model uses equations to calculate effort, time, and personnel needed based on project size. The intermediate model extends this by incorporating cost drivers. The detailed model further divides the project into phases and modules to provide more granular estimates.
2.6 Empirical estimation models & The make-buy decision.pptTHARUNS44
The document discusses empirical estimation models, including the structure of estimation models, the COCOMO II model, and the software equation. It also covers making a make/buy decision by creating a decision tree to calculate the expected cost of building, reusing, buying, or contracting a software project. Outsourcing is discussed as either a strategic or tactical decision.
Software_effort_estimation for Software engineering.pdfsnehan789
calculating software effort estimation for subjects like software engineering and software project management all according to your college preference on the subject
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.
The COCOMO model estimates the effort required for software projects in terms of person-months. It exists in three forms - basic, intermediate, and advanced. The basic model computes effort as a function of lines of code, while the intermediate model considers additional cost drivers like product attributes, hardware attributes, personal attributes, and project attributes. These attributes receive ratings that adjust the effort multiplier. The advanced COCOMO is an empirically derived model that requires extensive parameter calibration. All forms provide estimates of effort, schedule, and staff required for a software project.
This document provides an overview of the COCOMO (Constructive Cost Estimation Model) software cost estimation model. It discusses the basic, intermediate, and complete COCOMO models. The basic model estimates effort and development time as a function of source lines of code. It categorizes projects as organic, semidetached, or embedded based on characteristics of the product, team, and environment. The intermediate model refines the basic estimates using 15 cost drivers. The complete model treats a software project as composed of subsystems that may have different characteristics and complexity levels.
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.
This document discusses using a perceptron neural network model to implement the COCOMO II software cost estimation model. It begins with an introduction to software cost estimation and the COCOMO II model. It then provides details on neural networks and the perceptron learning rule. The proposed model uses the COCOMO II effort multipliers and scale factors as input to a three-layer perceptron network with one hidden layer. The weights are initially set based on the COCOMO II model and are updated using the perceptron learning rule. The model aims to provide more accurate effort estimates than COCOMO II alone by incorporating machine learning. Prior related work combining COCOMO II and neural networks is also discussed.
Plato and Aristotle's Views on Poetry by V.Jesinthal Maryjessintv
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Introduction to Literary Criticism 10 (1).pptxjessintv
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Chennai-600089
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