Fuzzy and Swarm Intelligence for Software Cost Estimation
Software cost estimation is the process of predicting the amount of time, effort and resources required to complete the project successfully. Software development is a collection of activities includes feasibility study, analysis, design, coding, testing, implementation and maintenance. Each phase requires resourcespeople, time, software and hardware which should be predicted well before the software development. The prediction means lot of uncertainty. So far many models are proposed by using Fuzzy Logic, Neural Networks, Machine Learning, Regression analysis and Soft Computing techniques. In this paper we are proposed a new model structure basing on Alaa F. Sheta using Fuzzy logic for controlling prediction uncertainty and the parameters of the cost model tuned by using swarm intelligence-Particle Swarm Optimization. The proposed model results are verified with NASA software dataset and results are compared with the existing models. The Results show that the value of MARE (Mean Absolute Relative Error) applying fuzzy-swarm intelligence was substantially lower than MARE of other models exists in the literature.
