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Learning From Developers: Towards Reliable Patch Validation at Scale for Linux

arXiv 2603.24825 ↗First posted 25 Mar 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies the past 10 years of patch reviews of the Linux memory management subsystem to characterize the challenges involved in patch reviewing at scale. Our study reveals that the review process is still primarily reliant on human effort despite a wide-range of automatic checking tools. Although kernel developers strive to review all patch proposals, they struggle to keep up with the increasing volume of submissions and depend significantly on a few developers for these reviews. To help scale the patch review process, we introduce FLINT, a patch validation system framework that synthesizes insights from past discussions among developers and automatically analyzes patch proposals for compliance. FLINT employs a rule-based analysis informed by past discussions among developers and an LLM that does not require training or fine-tuning on new data, and can continuously improve with minimum human effort. FLINT uses a multi-stage approach to efficiently distill the essential information from past discussions. Later, when a patch proposal needs review, FLINT retrieves the relevant validation rules for validation and generates a reference-backed report that developers can easily interpret and validate. FLINT targets bugs that traditional tools find hard to detect, ranging from maintainability issues, e.g., design choices and naming conventions, to complex concurrency issues, e.g., deadlocks and data races. FLINT detected 2 new issues in Linux v6.18 development cycle and 7 issues in previous versions. FLINT achieves 21% and 14% of higher ground-truth coverage on concurrency bugs than the baseline with LLM only. Moreover, FLINT achieves a 35% false positive rate, which is lower than the baseline.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 25 Mar 2026.

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References · 17

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2023Let's Verify Step by Stepcited by 3,598
  5. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  6. 2024Qwen2.5-Coder Technical Reportcited by 1,151
  7. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  8. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  9. 2024Generative Verifiers: Reward Modeling as Next-Token Predictioncited by 465
  10. 2025Learning to Reason without External Rewardscited by 199
  11. 2025Reinforcement Learning with Rubric Anchorscited by 108
  12. 2025Reinforcing General Reasoning without Verifierscited by 76
5 more
  1. 2025Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewardscited by 47
  2. 2025BitsAI-CR: Automated Code Review via LLM in Practicecited by 22
  3. 2025KNighter: Transforming Static Analysis with LLM-Synthesized Checkerscited by 22
  4. 2024KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolutioncited by 12
  5. 2025Evaluating LLM-Based Regression Test Generationcited by 6

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