LLM-Driven Kernel Evolution: Automating Driver Updates in Linux
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911283561889792 |
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| author | Kharlamova, Arina Liu, Jiawen Zhang, Tianyi Yang, Xinrui Alqasimi, Humaid Sun, Youcheng Xue, Chun Jason |
| author_facet | Kharlamova, Arina Liu, Jiawen Zhang, Tianyi Yang, Xinrui Alqasimi, Humaid Sun, Youcheng Xue, Chun Jason |
| contents | Linux kernel evolution breaks drivers through API/ABI changes, semantic shifts, and security-hardening updates. We introduce DRIVEBENCH, an executable corpus of kernel$\rightarrow$driver co-evolution cases, and AUTODRIVER, a closed-loop, LLM-driven system for automating driver maintenance. The system integrates prompt engineering, multi-agent collaboration, static analysis, and iterative validation to ensure that generated patches are not only syntactically correct but also functionally and semantically consistent with kernel conventions. The corpus spans v5.10-v6.10 with 235 validated cases drawn from 612 candidates. In evaluation across 55 cases, AUTODRIVER achieves 56.4% compilation success; QEMU-based boot verification indicates that compiled patches preserve driver initialization in most instances. By releasing DRIVEBENCH and tooling, we enable reproducible research and a practical route to continuous, safe co-evolution of drivers with the Linux kernel. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Driven Kernel Evolution: Automating Driver Updates in Linux Kharlamova, Arina Liu, Jiawen Zhang, Tianyi Yang, Xinrui Alqasimi, Humaid Sun, Youcheng Xue, Chun Jason Software Engineering Artificial Intelligence Linux kernel evolution breaks drivers through API/ABI changes, semantic shifts, and security-hardening updates. We introduce DRIVEBENCH, an executable corpus of kernel$\rightarrow$driver co-evolution cases, and AUTODRIVER, a closed-loop, LLM-driven system for automating driver maintenance. The system integrates prompt engineering, multi-agent collaboration, static analysis, and iterative validation to ensure that generated patches are not only syntactically correct but also functionally and semantically consistent with kernel conventions. The corpus spans v5.10-v6.10 with 235 validated cases drawn from 612 candidates. In evaluation across 55 cases, AUTODRIVER achieves 56.4% compilation success; QEMU-based boot verification indicates that compiled patches preserve driver initialization in most instances. By releasing DRIVEBENCH and tooling, we enable reproducible research and a practical route to continuous, safe co-evolution of drivers with the Linux kernel. |
| title | LLM-Driven Kernel Evolution: Automating Driver Updates in Linux |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.18924 |