Software Cost Estimation Using Soft Computing Techniques
Estimating the work-effort and the schedule required to develop and/or maintain a software system is one of the most critical activities in managing software projects. Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the convenient techniques in software effort estimation field. However, the methodology used for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and accurate manner. Different techniques have, so far, been used like regression analysis, mathematical derivations, simulation, neural network, genetic algorithm, soft computing, fuzzy logic modelling etc. This paper aims to utilize soft computing techniques to improve the accuracy of software effort estimation. In this approach fuzzy logic is used with particle swarm optimization to estimate software development effort. The model has been calibrated on 30 projects, taken from NASA dataset. The results of this model are compared with COCOMO II and Alaa Sheta Model. The proposed model yields better results in terms of MMRE.
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