A systematic review of machine learning methods in software testing

Background: The quest for higher software quality remains a paramount concern in software testing, prompting a shift towards leveraging machine learning techniques for enhanced testing efficacy. Objective: The objective of this paper is to identify, categorize, and systematically compare the present studies on software testing utilizing machine learning methods. Method: This study conducts a systematic literature review (SLR) of 40 pertinent studies spanning from 2018 to March 2024 to comprehensively analyze and classify machine learning methods in software testing. The review encompasses supervised learning, unsupervised learning, reinforcement learning, and hybrid learning approaches. Results: The strengths and weaknesses of each reviewed paper are dissected in this study. This paper also provides an in-depth analysis of the merits of machine learning methods in the context of software testing and addresses current unresolved issues. Potential areas for future research have been discussed, and statistics of each review paper have been collected. Conclusion: By addressing these aspects, this study contributes to advancing the discourse on machine learning's role in software testing and paves the way for substantial improvements in testing efficacy and software quality.