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COST EVALUATION FRAMEWORK OF EFFORT ESTIMATION MODELS
2
Author(s):
ANKITA , ALANKRITA AGGARWAL
Vol - 6, Issue- 7 ,
Page(s) : 39 - 48
(2015 )
DOI : https://doi.org/10.32804/IRJMST
Abstract
Software effort estimation is a vital task in software engineering. The importance of effort estimation becomes critical during early stage of the software life cycle when the details of the software have not been revealed yet. The effort involved in developing a software product plays an important role in determining the success or failure. There are basically some points approaches, which are available for software effort estimation such as Function Point, Use Case Point, Class Point, Object Point, etc. In this study, the main goal is to estimate the effort of various software projects using Class Point Approach. The parameters are optimized using various artificial intelligence (AI) techniques such as Multi-Layer Perceptron (MLP), K nearest Neighbor Regression (KNN) and Radial Basis Function Network(RBFN) such as to achieve better accuracy. Furthermore, a comparative analysis of software effort estimation using these various AI techniques has been provided. By estimating the software projects accurately, we can have software with acceptable quality within budget and on planned schedules.
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