一句话：提出并评估能自主提出、排序并完成企业级代码改进任务的 GenAI agent RACER，证明自主完善性维护可显著节省工作量。
论文关注自主软件维护中 agent 需自行决定处理哪些代码改进任务的问题，而不仅是被动解决人类提出的问题。作者开发了 GenAI agent RACER，使其能在企业范围内自主制定、排序并完成有限的重构与完善性维护任务。评估从两方面展开：一是提出方法，将 agent 引入任务及维护投入的时间与人工执行对比，以估计工作量节省；二是比较人工与 agent 驱动的代码改进对未来代码库维护的影响。结果显示，即使计入 agent 开发成本，仍有显著工作量节省；双重差分分析表明，自动改进对未来维护工作量的降低效果与人工改进相当甚至更强，验证了自主维护的可行性。
Automating software development is an important aspect of software engineering. Generative Artificial Intelligence (GenAI) Agents that control multiple tools and use feedback with some autonomy to iteratively solve problems are emerging as promising tools for increased software production. To achieve autonomous software maintenance with minimal human involvement, such agents must not only solve human-posed problems but also decide which problems to address. We report our experiences developing a GenAI agent that autonomously formulates, prioritizes, and completes a limited set of code improvement tasks enterprise-wide, and we evaluate this agent from two perspectives: first, developing a methodology to estimate effort savings from such autonomous agents by comparing time spent on agent-introduced tasks and agent maintenance efforts against manual execution; and second, comparing the impact of manual versus agent-driven code improvements on future codebase maintenance. Our results demonstrate significant effort savings, even after accounting for effort in agent development, and a Difference-in-Differences (DiD) analysis found that automated improvements had an effect on the reduction of future maintenance effort that was as strong as or stronger than manual improvements confirming the feasibility of autonomous maintenance. While the relaxed completion and timeliness needs of perfective maintenance make it an ideal candidate for autonomy, a degree of autonomy may be achievable in other areas of software development as well.
@inproceedings{MockusBBJGGLLLM26,
title = {Autonomous Refactoring for Perfective Maintenance at Scale: The Agentic RACER System and its Empirical Evaluations},
author = {Audris Mockus and Arjun Singh Banga and Payal Bhuptani and Sky Jazayeri and Jun Ge and Gurinder Grewal and Emma Lai and Jianmin Li and Haofeng Liang and Rishab Mangla and Vishal Parekh and Sergey Parshin and Peter C. Rigby and Gursharan Singh and Matt Steiner and Nachiappan Nagappan},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805211}
}