MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
Authors: name: Qihao Wang, name: Ziming Cheng, name: Shuo Zhang, name: Fan Liu, name: Rui Xu, name: Heng Lian, name: Kunyi Wang, name: Xiaoming Yu, name: Jianghao Yin, name: Sen Hu, name: Yue Hu, name: Shaolei Zhang, name: Yanbing Liu, name: Ronghao Chen, name: Huacan Wang
Organization: QuantaAlpha

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.