Code large language models (LLMs) have shown impressive capabilities on a multitude of software engineering tasks. In particular, they have demonstrated remarkable utility in the task of code repair. However, common benchmarks used to evaluate the performance of code LLMs are often limited to small-scale settings. In this work, we build upon kGym, which shares a benchmark for system-level Linux kernel bugs and a platform to run experiments on the Linux kernel. This paper introduces CrashFixer, the first LLM-based software repair agent that is applicable to Linux kernel bugs. Inspired by the typical workflow of a kernel developer, we identify the key capabilities an expert developer leverages to resolve a kernel crash. Using this as our guide, we revisit the kGym platform and identify key system improvements needed to practically run LLM-based agents at the scale of the Linux kernel (50K files and 20M lines of code). We implement these changes by extending kGym to create an improved platform - called kGymSuite, which will be open-sourced. Finally, the paper presents an evaluation of various repair strategies for such complex kernel bugs and showcases the value of explicitly generating a hypothesis before attempting to fix bugs in complex systems such as the Linux kernel. We also evaluated CrashFixer's capabilities on still open bugs, and found at least two patch suggestions considered plausible to resolve the reported bug.

本研究针对现有代码大型语言模型（LLMs）在处理Linux内核崩溃时面临的局限性，提出了一种新的软件修复代理CrashFixer。通过借鉴内核开发者的工作流程，本文构建了一个改进的平台kGymSuite，并展示了通过生成假设来解决复杂系统中的内核错误的重要性。研究表明，CrashFixer能够对尚未解决的错误提供至少两种合理的修补建议，具有潜在的实际应用价值。

CrashFixer：Linux内核崩溃解决代理