用快慢思维提升代码修复能力，自动适配不同难度问题
SIADAFIX: issue description response for adaptive program repair
- 结合快慢思维：快速判断问题类型，慢速精细修复
- 在SWE-bench Lite上达60.67%的pass@1，领先开源方法
- 适合需要高效精准修复的开发者与自动化测试场景
我们提出利用快思考与慢思考协同机制，增强大语言模型在复杂任务如程序修复中的表现。设计了一种基于问题描述响应的自适应修复方法SIADAFIX：慢思考的缺陷修复代理负责处理复杂修复任务，快思考的工作流决策组件则优化并分类问题描述，以结果指导修复流程编排。SIADAFIX根据问题复杂度自适应选择三种修复模式——简单、中等、复杂模式，对简单问题采用快速泛化，对复杂问题使用测试时缩放技术。在SWE-bench Lite上的实验表明，使用Claude-4 Sonnet模型时，该方法达到60.67%的pass@1性能，为所有开源方法中的最优水平。SIADAFIX有效平衡了修复效率与准确率，为自动化程序修复提供了新思路。代码已公开于https://github.com/liauto-siada/siada-cli。
原文摘要 · Abstract (English)
We propose utilizing fast and slow thinking to enhance the capabilities of large language model-based agents on complex tasks such as program repair. In particular, we design an adaptive program repair method based on issue description response, called SIADAFIX. The proposed method utilizes slow thinking bug fix agent to complete complex program repair tasks, and employs fast thinking workflow decision components to optimize and classify issue descriptions, using issue description response results to guide the orchestration of bug fix agent workflows. SIADAFIX adaptively selects three repair modes, i.e., easy, middle and hard mode, based on problem complexity. It employs fast generalization for simple problems and test-time scaling techniques for complex problems. Experimental results on the SWE-bench Lite show that the proposed method achieves 60.67% pass@1 performance using the Claude-4 Sonnet model, reaching state-of-the-art levels among all open-source methods. SIADAFIX effectively balances repair efficiency and accuracy, providing new insights for automated program repair. Our code is available at https://github.com/liauto-siada/siada-cli.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。