Using Bio-Inspired Features Selection Algorithms in Software Effort Estimation: A Systematic Literature Review

Abstract

Feature selection algorithms select the best and relevant set of features of the datasets which leads to an increase in the accuracy of predictions when employed with the machine learning techniques. Different feature selection algorithms are used in the domain of Software Development Effort Estimations (SDEE) and recently the use of bio-inspired feature selection algorithms got the attention of the researchers, which provided the best results in terms of the accuracy measures. In this paper, we manage to systematically evaluate and assess different bio-inspired feature selection algorithms which have been employed and investigated in the studies related to SDEE with the aim of increasing the accuracy of estimations. To the best of our knowledge, there is no Systematic Literature Review (SLR) which investigated the use of bio-inspired algorithms in SDEE. Since, the use of bio-inspired algorithms in the area of SDEE started in the late 2000, we have considered the studies published between 2007-2018. We have selected about 30 different studies from five digital libraries, i.e., IEEE explore, Springer, ScienceDirect, ACM digital library, and Google Scholar, after the filtering of inclusion/exclusion and quality assessment criteria. The main findings of our SLR are that Genetic Algorithms (GA) and Particle Swarm Optimizations (PSO) are widely used bio-inspired algorithms. Moreover, GA and PSO are the algorithms which outperform baseline estimation techniques (estimation techniques employed without any feature selection algorithms) in more number of experiments, in terms of prediction accuracy.