AI 中文总结
研究针对基于大语言模型的编码代理解决软件问题易因仓库理解不足出错的问题，提出ACQUIRE框架，通过问答驱动在修复前获取仓库知识，经两阶段解耦知识获取与补丁生成，实验证明该框架能提升问题解决准确性。
AI 中文摘要
基于大语言模型的编码代理显著推进了自动化软件问题解决，但因对仓库理解不足极易出现事实性错误。近期方法试图通过修复前的仓库探索来缓解此限制，但其修复驱动策略在未识别代理知识差距的情况下探索仓库，常产生不精确的上下文，无法弥合潜在的理解缺陷。本文提出ACQUIRE，一个用于软件问题解决的基于问答的框架。它在修复前明确获取仓库知识，通过两个阶段将知识获取与补丁生成解耦。第一阶段，提问者和回答者协作获取结构化仓库知识；第二阶段，解决者利用所得问答知识生成明智的补丁。通过将隐式知识差距转化为明确、事实可靠的理解，ACQUIRE加速了知识密集型修复阶段并实现更准确的解决。在SWE-bench Verified上的实验表明，ACQUIRE始终优于代表性的修复前方法，在适度增加成本和时间的情况下，使Pass@1提高多达4.4个百分点。
英文摘要
LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation through pre-repair repository exploration; however, their fix-driven strategies explore repositories without identifying the agent's knowledge gaps, often yielding imprecise context that fails to bridge the underlying understanding deficit. In this paper, we propose ACQUIRE, a QA-driven framework for software issue resolution. Mirroring how experienced developers first comprehend unfamiliar code before attempting a fix, ACQUIRE explicitly acquires repository knowledge prior to repair. The framework decouples knowledge acquisition from patch generation through two stages: in the first stage, a Questioner and an Answerer collaborate to acquire structured repository knowledge, where the Questioner poses targeted questions and the Answerer produces evidence-grounded answers through autonomous exploration; in the second stage, the Resolver leverages the resulting QA knowledge to generate informed patches. By transforming implicit knowledge gaps into explicit, factually reliable understanding, ACQUIRE accelerates knowledge-intensive repair stages and enables more accurate resolution. Experiments on SWE-bench Verified demonstrate that ACQUIRE consistently outperforms representative pre-repair methods, raising Pass@1 by up to 4.4 percentage points with modest additional cost and time.
