LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

Abstract

One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of aproject in the initial stage, always encounters several challenges and problems.However, due to the high significance and impact of the proper cost estimation,several approaches and methods have been proposed regarding how to perform costestimation, in which the analogy-based approach is one of the most popular ones. Inrecent years, many attempts have been made to employ suitable techniques andmethods in this approach in order to improve estimation accuracy. However,achieving improved estimation accuracy in these techniques is still an appropriateresearch topic. To improve software development cost estimation, the current studyhas investigated the effect of the LEM algorithm on optimization of featuresweighting and proposed a new method as well. In this research, the effectiveness ofthis algorithm has been examined on two datasets, Desharnais and Maxwell.Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate andcompare the proposed method against other evolutionary algorithms. Employing theproposed method showed considerable improvement in estimating software costestimation