Particle swarm optimization based effort estimation using Function Point analysis

Abstract

Software development effort estimation is one of the most important activities in software project management. Various models have been proposed to construct a relationship between software size and effort, however there are many problems. This is because project data, available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. Accurate estimation of the software effort and schedule affects the budget computation. Inaccurate estimates lead to failure of obtaining a profit, increased probability of project incompletion and delay of the project delivery date. Function Points (FP) are one of the size metrics which are used for estimating the effort of the project and Particle Swarm Optimization (PSO), a swarm intelligence technique is used to tune the parameters of Value Adjustment Factor (VAF) which is used to obtain the function count From this optimized function count, optimized Albrecht & Gaffney effort is estimated. The estimated effort is compared with the existing effort models and performance analysis is done on the basis of %MARE and RMSE. The research shows that the results of the proposed model are far better than the existing models.