For an example of a review (picked pretty much at random) see: https://sashiko.dev/#/patchset/20260318151256.2590375-1-andr...

The original patch series corresponding to that is: https://lkml.org/lkml/2026/3/18/1600

Edit: Here's a simpler and better example of a review: https://sashiko.dev/#/patchset/20260318110848.2779003-1-liju...

I'm very glad they're not spamming the mailing list.

I think the table might be slightly inside-out? The Status column appears to show internal pipeline states ("Pending", "In Review") that really only matter to the system, while Findings are buried in the column on the far right. For example, one reviewed patchset with a critical and a high finding is just causally hanging out below the fold. I couldn't immediately find a way to filter or search for severe findings.

It might help to separate unreviewed patches from reviewed ones, and somehow wire the findings into the visual hierarchy better. Or perhaps I'm just off base and this is targeting a very specific Linux kernel community workflow/mindset.

Just my 1c.

That's cool. Another interesting metric, however, would be the false positive ratio: like, I could just build a bogus system that simply marks everything as a bug and then claim "my system found 100% of all bugs!"

In practice, not just the recall of a bug finding system is important but also its precision: if human reviewers get spammed with piles of alleged bug reports by something like Sashiko, most of which turn out not to be bugs at all, that noise binds resources and could undermine trust in the usefulness of the system.

(Also tests can be focused per defect.. which prevents overload)

From some of the changes I'm seeing: This looks like it's doing style and structure changes, which for a codebase this size is going to add drag to existing development. (I'm supportive of cleanups.. but done on an automated basis is a bad idea)

I.e. https://sashiko.dev/#/message/20260318170604.10254-1-erdemhu...

Seems to be a well funded effort though so maybe it’s better?

What does this mean?

Nitpicking on this though:

> "In my measurement, Sashiko was able to find 53% of bugs based on a completely unfiltered set of 1000 recent upstream issues based on "Fixes:" tags (using Gemini 3.1 Pro). Some might say that 53% is not that impressive, but 100% of these issues were missed by human reviewers."

That'd assume 100% of the issues that were fixed and used for training were not fixed following a human review. I don't buy it: it's extremely common to have a dev notice a bug in the code, without a user having ever reported the bug.

I think the wording meant to say: "... but 100% of these issues were first missed by humans".

My point being: the original code review by a human ain't the only code review by a human. Or put it this way: it's not as if we were writing code, shipping it, then never ever looking at that line of code again unless a bug report were to come out. It's not how development works.

We've already seen how bug bounty projects were closed by AI spam; I think it was curl? Or some other project I don't remember right now.

I think AI tools should be required, by law, to verify that what they report is actually a true bug rather than some hypothetical, hallucinated context-dependent not-quite-a-real-bug bug.