A systematic review of machine learning methods in software testing

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. The objective of this paper is to identify, categorize, and systematically compare the present studies on software te...

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Bibliographic Details
Published in:Applied soft computing 2024-09, Vol.162, p.111805, Article 111805
Main Authors: Ajorloo, Sedighe, Jamarani, Amirhossein, Kashfi, Mehdi, Haghi Kashani, Mostafa, Najafizadeh, Abbas
Format: Article
Language:English
Subjects:
Machine learning
Quality of software
Software testing
Systematic review
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Summary: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. The objective of this paper is to identify, categorize, and systematically compare the present studies on software testing utilizing machine learning methods. 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. 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. 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. •A comprehensive systematic review on machine learning methods in software testing is provided.•The main ideas, methods, tools, merits, demerits, evaluation metrics, and evaluation methods are discussed.•A scientific taxonomy of machine learning methods in software testing is presented.•A detailed list of challenges, open issues, and future research directions is outlined.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2024.111805