Current Software Defect Prediction: A Systematic Review

Abstract

detecting defect in a software product prior to testing reduces the cost of testing and improve the quality of the software product. Various methods for enhancing the accuracy of defect prediction model have been published. The goal of this review is to identify and analyze the dataset, models, framework and the performance of software defect prediction model. The IEEExplore, Science Direct, Scopus, and Google Scholar databases were used to search and download the relevant papers. Sixty-eight (68) papers published between 2017 to 2021 were selected based on exclusion and inclusion criteria. Analysis of the selected studies revealed that 100% of the selected articles used the publicly available dataset from NASA, PROMISE and others. The most frequently used models in software defect prediction studies were identified. The analysis also revealed IEEE Transactions on Software Engineering Journal is the most significant journal with respect to software defect prediction studies using Scimago Journal Ranking as criteria. The review also identified studies on enhancing the predictive performance of defect prediction models. Software defect prediction is still active and sound. Thus, needs more research especially on enhancement methods.