Patch backporting, the process of migrating mainline security patches to older branches, is an essential task in maintaining popular open-source projects (e.g., Linux kernel). However, manual backporting can be labor-intensive, while existing automated methods, which heavily rely on predefined syntax or semantic rules, often lack agility for complex patches. In this paper, we introduce PORTGPT, an LLM-agent for end-to-end automation of patch backporting in real-world scenarios. PORTGPT enhances an LLM with tools to access code on-demand, summarize Git history, and revise patches autonomously based on feedback (e.g., from compilers), hence, simulating human-like reasoning and verification. PORTGPT achieved an 89.15% success rate on existing datasets (1815 cases), and 62.33% on our own dataset of 146 complex cases, both outperforms state-of-the-art of backporting tools. We contributed 9 backported patches from PORTGPT to the Linux kernel community and all patches are now merged.

翻译：补丁回传是将主线安全补丁迁移至旧版本分支的过程，是维护流行开源项目（如Linux内核）的关键任务。然而，人工回传工作繁重，而现有自动化方法过度依赖预定义的语法或语义规则，往往难以灵活处理复杂补丁。本文提出PORTGPT，一种面向实际场景的端到端补丁回传自动化LLM智能体。PORTGPT通过工具增强大型语言模型，实现按需访问代码、总结Git历史记录，并能基于反馈（如编译器输出）自主修订补丁，从而模拟类人推理与验证过程。在现有数据集（1815个案例）上，PORTGPT成功率达89.15%；在我们自建的146个复杂案例数据集上达到62.33%，均优于当前最先进的回传工具。我们已将PORTGPT生成的9个回传补丁贡献至Linux内核社区，所有补丁均已被合并。