AN EFFICIENT APPROACH OF SOFTWARE COST ESTIMATION USING SOFT COMPUTING TECHNIQUES

Software development cost estimation is an important activity in the early software design phases. The input datasets are primarily taken from the promise repository. Data mining an soft computing techniques are used to assess the software development cost estimation. Each feature in the input dataset is divided, the linguistic terms along with the membership are identified using trapezoidal membership functions, and associative classification is adopted for generating rules. A large number of rules is filtered with respect to the support and confidence. Genetic algorithm is employed as an optimization tool for selecting the best rules. The example presented demonstrates the improvement in accuracy. The crisp costs are presented after defuzzification of the output. This paper aims to utilize an adaptive fuzzy logic model to improve the accuracy of software time and cost estimation. Using advantages of fuzzy set and fuzzy logic can produce accurate software attributes which result in precise software estimates. 63 Historic projects of NASA dataset having COCOMO format is used in the evaluation of the proposed Fuzzy Logic COCOMO II.