Speaker
Description
Linux kernel bug reports vary widely in quality: some are duplicates or false positives, while others describe exploitable vulnerabilities or issues with little practical impact. Maintainers still need to determine what is real, what matters, and what deserves attention first.
At the same time, bug findings are increasingly fragmented across different tools and workflows, including fuzzers, static analyzers, human analysis, AI-assisted tools, and patch-review systems. For example, Sashiko may uncover pre-existing bugs while reviewing patches, but those findings can remain scattered across individual review reports rather than being collected and tracked in one place.
We are developing a continuous Linux kernel bug monitoring and veirfy platform. The platform aggregates findings from multiple sources, maps them to kernel subsystems, and gives maintainers a view of currently observed bugs affecting the code they maintain. For each finding, it attempts to generate and execute a reproducer on a KASAN-enabled kernel to help validate the report. It also assesses potential impact. For example, whether a bug may enable privilege escalation or has limited practical impact. Confirmed bugs can also include AI-generated analysis and draft patches to help reduce the effort required to understand and fix the issue.
Building on the mailing-list discussion[1], we would like to explore what actionable kernel bug observability should look like. How should heterogeneous bug signals be validated, prioritized, and presented to subsystem maintainers? How can automated analysis help distinguish important bugs from noise without creating yet another stream of low-quality reports?
[1] https://lore.kernel.org/all/20260830115546.3942129-1-yuantan098@gmail.com/