PortGPT: Towards Automated Backporting Using Large Language Models

• URL: http://arxiv.org/abs/2510.22396v1
• Date: Sat, 25 Oct 2025 18:46:04 GMT
• Title: PortGPT: Towards Automated Backporting Using Large Language Models
• Abstract summary: We introduce PORTGPT, an agent for end-to-end automation of patch backporting in real-world scenarios.<n> PORTGPT enhances an LLM with tools to access code on-demand, summarize Git history, and revise patches autonomously based on feedback.<n>We contributed 9 backported patches from PORTGPT to the Linux kernel community and all patches are now merged.
• Score: 16.654479541363802
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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