Analogy Estimation Based on Particle Swarm Optimization and Bootstrap Inference for Software Effort
Chen Cai · Journal of management science · 2010
Wide attention has been attracted on software effort estimation by both software industry and academic community owing to its high ability in controlling software schedule,reducing software risk and guaranteeing software quality.This paper investigates the improvement effects of estimation accuracy in analogy-based model when particle swarm optimization method is adopted to optimize the feature weights.Meanwhile,nonparametric bootstrap was employed to generate samples for calibrating the number of the most similar projects and the project adaptation in PSO analogy model.Reliability of estimated value and confidence interval were calculated by nonparametric bootstrap too.Experiments were carried out using software projects from Desharnais dataset in order to verify the effectiveness of PSO analogy model and bootstrap inference method.Estimation accuracy of the PSO analogy model was compared with ordinary Analogy,SVR,ANN,RBF and CART in terms of the error measure which is MMRE and Pred(0.25).The empirical results show that applying particle swarm optimization method to optimize the feature weights is a feasible approach to improve the accuracy of software effort estimation.Moreover,nonparametric bootstrap can calibrate the variables in PSO analogy model and calculate estimation accuracy and confidence interval effectively.Results of the proposed model are beneficial to assess estimation accuracy,analyze risk and plan project.