MarsCode Agent: AI-native Automated Bug Fixing
Yizhou Liu, Pengfei Gao, Xinchen Wang, Jie Liu, Yexuan Shi, Zhao, Zhang, Chao Peng

TL;DR
MarsCode Agent is an innovative framework that uses large language models combined with code analysis to automatically identify, localize, and repair bugs in real-world software systems, demonstrating high success rates.
Contribution
It introduces a systematic approach integrating LLMs with code analysis for automated bug fixing, improving accuracy and effectiveness over existing methods.
Findings
High success rate in bug fixing on SWE-bench
Effective fault localization and patch generation
Outperforms most existing automated approaches
Abstract
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…
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Taxonomy
TopicsScientific Computing and Data Management
