Artificial Intelligence Techniques-Driven Automated Localization of Software Faults

Effective program debugging and software maintenance depend on automated software fault localization (ASSL). Manual analysis underlie traditional fault localization techniques, which are labor-intensive and prone to mistakes. Recent developments in artificial intelligence (AI) have brought interesting solutions that boost defect location and automation enhancement. A thorough investigation of AI-driven methods for Software Fault Localization (SFL) is given in this paper. It looks at Deep Learning (DL), Large Language Models (LLMs), Machine Learning (ML) techniques, and current area developments. Furthermore covered in the study are the limits of traditional methods, the benefits of AI-based approaches, and main difficulties and future directions in AI-driven fault localization.