TyPatch: Transforming Patches into Typestate Rules for Kernel Bug Detection
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Abstract (arXiv)
Historical Linux kernel patches capture defect knowledge that applies beyond their original repair sites. Recent work has shown that large language models (LLMs) can generate static-analysis checkers from historical patches and use them to uncover new kernel bugs. However, complete-checker generation requires the model both to recover the defect semantics expressed by a patch and to implement sophisticated program-analysis machinery, including object tracking, alias analysis, path-state maintenance, and interprocedural propagation. Coupling these responsibilities in a single end-to-end code-generation task can turn a simple defect rule into an unstable and expensive analyzer-implementation problem. To address this problem, we present TyPatch, which decouples patch-specific defect semantics from analyzer implementation. An LLM translates each patch into a typestate rule specifying its tracked object, actions, guards, transitions, and violations. A shared backend then executes these rules, binding their actions to program events, tracking object identity across aliases, propagating typestate along program paths, and producing reports for all rules. On Linux v6.16, TyPatch finds 559 distinct bugs, 121 of which have been confirmed by kernel developers. In a matched 38-patch comparison with the state-of-the-art complete-checker construction workflow, TyPatch uses 88.3-90.1% fewer generation tokens, while its initial report pools achieve 3.42-14.95× the precision of those produced by that workflow.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 46 references that are in the map. First posted on arXiv 12 Sep 2026.
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References · 4
- 2024IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilitiescited by 103
- 2019Getafix: Learning to Fix Bugs Automaticallycited by 41
- 2025KNighter: Transforming Static Analysis with LLM-Synthesized Checkerscited by 24
- 2025LAMeD: LLM-generated Annotations for Memory Leak Detectioncited by 2
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