Analogy-based software effort estimationusing multi-objective feature selection

The feature selection in analogy-based software effort estimation(ASEE)is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper-based feature selection method, multi-objective feature selection for analogy-based software effort estimation(MASE), is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE(mean magnitude of relative error), MdMRE(median magnitude of relative error), PRED(0.25), and SA(standardized accuracy)performance metrics.