Using Artificial Intelligence Techniques for Error Detection and Repair in Software Development
TL;DR
This study presents a comprehensive survey and comparative analysis of AI-based techniques for error detection and automated program repair, revealing that while LLM-based approaches significantly outperform traditional methods in repair capability, challenges remain in patch correctness validation, computational cost, and generalization to real-world codebases.
Abstract
Software bugs remain one of the most costly chal-lenge in software engineering, consuming significant development time and resources. Recent advances in Artificial Intelligence (AI), particularly deep learning and large language models (LLMs), have shown remarkable potential in automating the detection and repair of software errors. This study presents a comprehensive survey and comparative analysis of AI-based techniques for error detection and automated program repair (APR). We catego-rize existing approaches into traditional search-based methods, learning-based neural machine translation models, and emerg-ing LLM-based repair systems. We evaluate these techniques across standard benchmarks, including Defects4J and SWE-bench, comparing their effectiveness in terms of bugs fixed, patch correctness, and scalability. Our analysis reveals that while LLM-based approaches significantly outperform traditional methods in repair capability, challenges remain in patch correctness validation, computational cost, and generalization to real-world codebases. Our results show that LLM-based tools, such as ChatRepair, can correctly fix 114 out of 395 benchmark bugs at just $0.42 per fix, fixing 2.6× as many bugs as the best traditional method (a 165% increase). We discuss open challenges and propose future research directions toward more reliable AI-assisted software development.