[$] Reviewing kernel patches with LLMs
The 2026 Linux Storage, Filesystem, Memory Management, and BPF Summit featured discussions on the use of large language models (LLMs) for patch reviews in the kernel community. Roman Gushchin and others presented findings on the Sashiko tool, which aims to enhance code review processes. The tool has shown promising results in identifying high-severity issues, although it also presents challenges such as false positives and varying output quality.
- ▪The plenary session led by Roman Gushchin highlighted the increasing role of LLMs in kernel patch reviews.
- ▪Sashiko, a tool designed to assist in code reviews, has been noted for its high accuracy in detecting critical issues, achieving nearly 97% accuracy for high-severity problems.
- ▪There are currently 48 mailing lists utilizing Sashiko for patch reviews, but some maintainers have expressed concerns about the efficiency of this approach.
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Did you know...? LWN.net is a subscriber-supported publication; we rely on subscribers to keep the entire operation going. Please help out by buying a subscription and keeping LWN on the net. By Jake EdgeMay 25, 2026 LSFMM+BPF In a plenary session at the 2026 Linux Storage, Filesystem, Memory Management, and BPF Summit, the state of patch review using large language models (LLMs) was discussed. It is a topic that has been swirling around in the kernel community for much of the year. The plenary, which was led by Roman Gushchin, Chris Mason, Josef Bacik, and Sasha Levin, resulted in a quite bit of discussion, so much that a second filesystem-track-only (though others surely sat in) slot was used to continue it later in the day.
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