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As systems became more complex and new languages emerged, different software parametric models emerged that employed new cost estimating relationships, risk analyzers, software sizing, nonlinear software reuse, and personnel continuity. diagrams and thus provides an input to the traditional COCOMO method. These models were highly effective for waterfall model, version 1 software projects of the 1980s and highlighted the early achievements of parametrics. Most parametric software cost estimation models used today evolved in the late. The prime advantage of these models is that they are objective, repeatable, calibrated and easy to use, although calibration to previous experience may be a disadvantage when applied to a significantly different project. In the early 1980s refinements to earlier models, such as PRICE S and SLIM, and new models, such as SPQR, Checkpoint, ESTIMACS, SEER-SEM or COCOMO and its commercial implementations PCOC, Costimator, GECOMO, COSTAR and Before You Leap emerged. Software project managers use software parametric models and parametric estimation tools to estimate their projects' duration, staffing and cost. A scenario is defined by selecting a value for each parameter. ( Learn how and when to remove this template message)Ī parametric model is a set of related mathematical equations that incorporates variable parameters. WikiProject Computing may be able to help recruit an expert. Please add a reason or a talk parameter to this template to explain the issue with the article. Keywords: COCOMO, fuzzy, optimization.This article needs attention from an expert in Computing.
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The example provided continues the project scenario to demonstrate optimization of parameters to satisfy the objective. Specification of appropriate constraint functions and an objective function allows application of fuzzy optimization methods. Given such a project for which the method is applied, one may ask whether some augmentation of one or more parameters might optimize the COCOMO result toward a desired combination of schedule and effort. Such a project scenario is presented and the method is applied to demonstrate its use. By application of constraints created by dictated fuzzy results, and back propagation, better estimates of project parameters are obtainable. This paper demonstrates how one may use a dictated fuzzy schedule and budget to improve an f-COCOMO Model, or plan a project.
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It is not likely that the fuzziness of the project parameters (linguistic variables) will produce a COCOMO result that is contained within the forces-estimated schedule or budget. Other forces, such as time-to-market pressure or corporate goals, determine an estimated (fuzzy) schedule and budget even before conceptualization is complete.
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Current resources and/or policy may determine other parameters. When a project is planned, software management uses prior history and software engineers’ opinions to estimate some parameters of the proposed project. An example which demonstrates how fuzzy parameters affect f-COCOMO results is presented. Of course this is all sensible, since standard COCOMO parameters begin with linguistic variables. With a fuzzy project schedule, or effort (budget), some one or more parameters necessarily must be fuzzy. There is some possibility that it will complete in February. For example, if a project must be complete by March 15, there is some possibility that it will not complete until the end of March. However, even deadlines are fuzzy objects. Most software projects have deadlines dictated by management or market. If a project parameter is fuzzy, the associated COCOMO Model becomes a fuzzy COCOMO Model (f-COCOMO Model) with a fuzzy result (schedule and effort). Nevertheless, in the case of software cost estimation using COCOMO, we find and show that this characteristic of fuzzy arithmetic may be used to advantage. This makes fuzzy results of some computations too fuzzy to be useful. As is known in fuzzy circles and is shown here, fuzzy arithmetic based on the popular fuzzy extension principle may produce unacceptable results under fuzzy multiplication. Barry Boehm at the University of Southern California (USC) COCOMO II is an open model, so all of the details are published There are different versions of the model, the Early Design Model the Post. This basis in linguistic variables encourages research of the COCOMO Model as a fuzzy system. Parametric Model COCOMO II COCOMO II is a tool developed by the Center for Software Engineering (CSE), headed by Dr. It allows one to work from linguistic variables to estimate software project effort and schedule. Reilly a OctoUniversity of Alabama at Birmingham a Department of Computer and Information Sciences, b Department of Mathematics, Birmingham, Alabama, 35294, USA Abstract The COCOMO Model is well known as the currently predominate model for software cost estimation. Estimation of f-COCOMO Model Parameters Using Optimization Techniques Leonard J.