Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Abstract

Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and dataset analysis. This study provides a systematic literature review that highlights the latest research trends in the area of SDP by providing a critical review of papers published between 2016 and 2019. Initially, 1012 papers were shortlisted from three online libraries (IEEE Xplore, ACM, and ScienceDirect); following a systematic research protocol, 22 of these papers were selected for detailed critical review. This review will serve researchers by providing the most current picture of the published work on software defect classification.