Advancements in mutation testing: Enhancing software fault detection with AI and fuzzing techniques
Department of Software Quality Engineering Apple Inc, USA.
Research Article
Global Journal of Engineering and Technology Advances, 2023, 15(02), 150-160.
Article DOI: 10.30574/gjeta.2023.15.2.0102
Publication history:
Received on 21 April 2023; revised on 26 May 2023; accepted on 29 May 2023
Abstract:
the strength of test cases. In the last decades, the refinement of different AI and fuzzing techniques has given mutation testing the heft to do software fault detection efficiently and effectively. AI approaches apply machine learning algorithms to streamline the mutant generation processes while concurrently prioritizing salient test cases, thus minimizing computation while maximizing fault detection. The complementary role of fuzzing in mutation testing comes from its systematic examination of edge cases that can expose the weaknesses of more complex software systems. This research deals with AI-fuzzing mutation testing, focusing on key milestones, challenges, and future directions. Findings portray that AI mutation testing has led to more automation, reduced equivalent mutants, and better reliability of the software. However, challenges to this technology remain in terms of computation, false positives, and ethical issues. Future research must work towards improved AI models for mutation testing while concentrating on developing the automation frameworks of mutation testing, keeping in mind security-related issues to achieve effective integration in present-day software development.
Keywords:
Mutation testing; Software fault detection; Artificial intelligence; Fuzzing techniques; Test case optimization; Software reliability; Machine learning; Automated testing; Vulnerability detection; Software quality assurance
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0