Ndlovu, LungisaniNtshangase, Ntomfuthi LMtshali, Mamello LMakokoe, Mmamosweu PCMyaka, Zanele SNdhlovu, NomalisaKhumalo, Lethukuthula PCossa, Bheki2025-02-262025-02-262024-07979-8-3503-9591-4DOI: 10.1109/ICECET61485.2024.10698379http://hdl.handle.net/10204/14089With the rise of software applications, software development has become a rapidly evolving field, propelled by technological advancements and the increasing demand for innovative solutions to sustain our digital era. Consequently, detecting software faults has emerged as a critical attribute in the software development cycle to ensure system reliability, quality, and user satisfaction. However, traditional fault detection methods often suffer from drawbacks such as time consumption and error proneness, particularly in the context of large and complex software systems. In response to these hurdles, artificial intelligence (AI) presents itself as a promising methodology, with the aim of improving accuracy, scalability, automation, and proactiveness in fault detection. Therefore, this paper systematically reviews the literature on AI-based software fault detection algorithms. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a curated set of 50 peer-reviewed research publications was identified in Google Scholar and subsequently analysed using a classification coding framework. The findings reveal that AIbased software fault detection algorithms play an important role in improving software system reliability and performance.AbstractenSoftware developmentArtificial intelligenceAI Fault detectionSystem reliabilityPreferred Reporting Items for Systematic Reviews and Meta-AnalysesPRISMAConference Presentation