用树搜索提升大模型修复代码的精准度
$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
- 结合大模型推理与树搜索生成修复方案
- 在多个基准上修复成功率显著提升
- 适合需要高效自动化调试的开发者
自动程序修复（APR）是软件开发与维护的核心技术，旨在实现缺陷的自动化修复，减少人工干预。近年来，大型语言模型（LLMs）和思维链（CoT）技术的进展显著增强了模型的推理能力。然而，由于程序修复所需的复杂逻辑和多步推理，CoT技术在APR领域的应用仍不充分。本研究系统评估了多种常见CoT技术在APR任务中的表现，并提出一种创新框架T³，将大模型的强大推理能力与树搜索相结合，有效提升了候选修复方案的生成精度。此外，T³为优化样本选择和修复策略提供了重要指导，建立了高效自动化调试的可靠框架。
原文摘要 · Abstract (English)
Automatic Program Repair (APR) is a core technology in software development and maintenance, with aims to enable automated defect repair with minimal human intervention. In recent years, the substantial advancements in Large Language Models (LLMs) and the Chain-of-Thought (CoT) techniques have significantly enhanced the reasoning capabilities of these models. However, due to the complex logic and multi-step reasoning ability needed, the application of CoT techniques in the APR domain remains insufficient. This study systematically evaluates the performance of several common CoT techniques in APR tasks and proposes an innovative framework $T^3$, which integrates the powerful reasoning capabilities of LLMs with tree search, effectively improving the precision of generating candidate repair solutions. Furthermore, $T^3$ provides valuable guidance for optimizing sample selection and repair strategies in APR tasks, establishing a robust framework for achieving efficient automated debugging.
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