Uzunbayir, S.Kurtel, K.2025-02-252025-02-2520249798350365887https://doi.org/10.1109/UBMK63289.2024.10773427https://hdl.handle.net/20.500.14365/5931Mutation testing is a widely accepted method for assessing the effectiveness of software test suites. It focuses on evaluating how well a test suite can identify deliberately introduced faults, known as mutations, in the code, helping to reveal potential vulnerabilities. Traditional mutation testing approaches, however, often encounter significant issues such as high computational demands and limited fault detection range. Recently, there has been an increasing interest in integrating artificial intelligence (AI) into mutation testing to address these challenges. AI can enhance mutation testing by incorporating intelligent algorithms and automating various tasks. It can analyze the codebase, identify key program components, and strategically select mutation operators that are more likely to detect faults. This paper explores the fundamental concepts of mutation testing, relevant research, and innovations in combining AI with mutation testing, focusing on the challenges and the most effective models currently available. By integrating AI, mutation testing has seen improvements in both fault detection and computational efficiency. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessArtificial IntelligenceMutation TestingSoftware TestingTest AutomationConference Object10.1109/UBMK63289.2024.107734272-s2.0-85215533527