Problem
Research questions and friction points this paper is trying to address.
Agentic AI
Large Language Models
GitHub issues
incorrect localization
incomplete validation
XAgent: eXecution-guided Agentic AI for Effective Localization and Resolution of GitHub Issues
🤖 AI Summary
本文提出XAgent，通过分析动态行为和额外程序上下文来定位和验证GitHub问题，解决了现有方法因依赖静态描述导致的问题定位不准确和验证不完全的难题。
📝 Abstract
Agentic AI has enabled capabilities in leveraging Large Language Models (LLMs) to autonomously resolve repository-level GitHub issues. However, due to the reliance on limited static description of issues, existing agentic approaches suffer from incorrect localization and incomplete validation. Solely relying on this information can bias LLM reasoning toward the narrow scope of the issue description, leading to incomplete patches that fail to address the underlying issue. In this paper, we present XAgent, an execution-guided agentic framework that analyzes dynamic behavior and additional program context to localize and validate issues. The experimental results on the SWE-bench-lite dataset demonstrate that XAgent outperforms other existing approaches, achieving a resolve rate of 62.0% and a function localization accuracy of 72.8%, while maintaining cost efficiency. Our analysis further shows that XAgent successfully resolves 7 additional issues that the top existing baselines fail to address. This work highlights a shift from static, description-oriented patch generation toward dynamic execution-guided issue resolution, opening new opportunities for LLM-based coding agents to achieve more robust and generalizable software maintenance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
execution-guided
dynamic behavior
program context
localization and validation
agentic AI
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