Advancements in mutation testing: Enhancing software fault detection with AI and fuzzing techniques

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.