# MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions

arXiv: https://arxiv.org/abs/2504.09474 · HTML: https://mlsanity.com/paper/2504.09474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://mlsanity.com/paper/2207.01780/) (2022)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [Code Prediction by Feeding Trees to Transformers](https://mlsanity.com/paper/2003.13848/) (2020)
- [CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation](https://mlsanity.com/paper/2310.04951/) (2023)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences](https://mlsanity.com/paper/2202.06689/) (2022)
- [Bidirectional Language Models Are Also Few-shot Learners](https://mlsanity.com/paper/2209.14500/) (2022)
- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [Augmenting Greybox Fuzzing with Generative AI](https://mlsanity.com/paper/2306.06782/) (2023)
- [Learning Syntactic Program Transformations from Examples](https://mlsanity.com/paper/1608.09000/) (2016)
- [KernelGPT: Enhanced Kernel Fuzzing via Large Language Models](https://mlsanity.com/paper/2401.00563/) (2023)
- [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024)
- [BlockScope: Detecting and Investigating Propagated Vulnerabilities in Forked Blockchain Projects](https://mlsanity.com/paper/2208.00205/) (2022)
- [Automatic Android Deprecated-API Usage Update by Learning from Single Updated Example](https://mlsanity.com/paper/2005.13220/) (2020)
- [Automating Zero-Shot Patch Porting for Hard Forks](https://mlsanity.com/paper/2404.17964/) (2024)
- [BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent](https://mlsanity.com/paper/2503.09663/) (2025)

## Built on by (most cited first, 2 of 2)

- [LLM-Driven Kernel Evolution: Automating Driver Updates in Linux](https://mlsanity.com/paper/2511.18924/) (2025)
- [Benchmarking Automated Security Patch Backporting: How Far Are We?](https://mlsanity.com/paper/2608.17671/) (2026)

## Abstract (arXiv)

Out-of-tree kernel patches are essential for adapting the Linux kernel to new hardware or enabling specific functionalities. Maintaining and updating these patches across different kernel versions demands significant effort from experienced engineers. Large language models (LLMs) have shown remarkable progress across various domains, suggesting their potential for automating out-of-tree kernel patch migration. However, our findings reveal that LLMs, while promising, struggle with incomplete code context understanding and inaccurate migration point identification. In this work, we propose MigGPT, a framework that employs a novel code fingerprint structure to retain code snippet information and incorporates three meticulously designed modules to improve the migration accuracy and efficiency of out-of-tree kernel patches. Furthermore, we establish a robust benchmark using real-world out-of-tree kernel patch projects to evaluate LLM capabilities. Evaluations show that MigGPT significantly outperforms the direct application of vanilla LLMs, achieving an average completion rate of 74.07 for migration tasks.