While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench Verified by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.

翻译：尽管自主软件工程智能体正在重塑编程范式，但它们目前存在"封闭世界"的限制：它们试图从零开始或仅利用本地上下文修复错误，而忽略了在GitHub等平台上可用的海量历史人类经验。访问这种开放世界经验受到现实世界问题跟踪数据非结构化和碎片化性质的阻碍。本文提出MemGovern框架，旨在治理原始GitHub数据并将其转化为可供智能体使用的可操作经验记忆。MemGovern采用经验治理机制将人类经验转化为智能体友好的经验卡片，并引入智能体经验搜索策略，实现基于逻辑的人类专业知识检索。通过生成13.5万张受治理的经验卡片，MemGovern实现了显著的性能提升，将SWE-bench Verified上的问题解决率提高了4.65%。作为一种插件式方法，MemGovern为智能体友好的记忆基础设施提供了解决方案。