Machine learning implementation in automated software testing: a review

Awang Abu Bakar, Normi Sham (2025) Machine learning implementation in automated software testing: a review. Journal of Data Analytics and Artificial Intelligence Applications, 1 (1). pp. 110-122. E-ISSN 3062-2697

PDF (Article) - Published Version Restricted to Repository staff only Download (395kB) | Request a copy

Abstract

The integration of Machine Learning (ML) in automated software testing represents a transformative approach aimed at enhancing the efficiency, accuracy, and scope of testing processes. This paper explores the theoretical and practical aspects of employing ML techniques within the realm of software testing, focusing on key areas such as test case generation, defect prediction, and test suite optimisation. Through a comprehensive literature review and case studies, this study illustrates the potential benefits associated with ML-driven testing methodologies. The findings indicate that ML can significantly reduce manual intervention and improve defect detection rates, thereby facilitating more reliable software delivery. This paper also addresses the benefits of ML implementation in automated testing and future research directions to bridge existing gaps and further leverage ML in software testing

Item Type:	Article (Journal)
Uncontrolled Keywords:	Artificial intelligence, automated testing, software testing activities, machine learning algorithm
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Normi Sham Awang Abu Bakar
Date Deposited:	03 Mar 2025 15:57
Last Modified:	03 Mar 2025 15:57
URI:	http://irep.iium.edu.my/id/eprint/119961

Actions (login required)

View Item