MarsCode Agent: AI-native Automated Bug Fixing
This addresses the problem of automating bug fixing for software developers, representing an incremental improvement over existing methods.
The paper tackles automated bug fixing in software code by introducing MarsCode Agent, a framework that combines large language models with code analysis to localize faults and generate patches, achieving a high success rate on the SWE-bench benchmark.
Recent advances in large language models (LLMs) have shown significant potential to automate various software development tasks, including code completion, test generation, and bug fixing. However, the application of LLMs for automated bug fixing remains challenging due to the complexity and diversity of real-world software systems. In this paper, we introduce MarsCode Agent, a novel framework that leverages LLMs to automatically identify and repair bugs in software code. MarsCode Agent combines the power of LLMs with advanced code analysis techniques to accurately localize faults and generate patches. Our approach follows a systematic process of planning, bug reproduction, fault localization, candidate patch generation, and validation to ensure high-quality bug fixes. We evaluated MarsCode Agent on SWE-bench, a comprehensive benchmark of real-world software projects, and our results show that MarsCode Agent achieves a high success rate in bug fixing compared to most of the existing automated approaches.