Speaker
Description
Linux kernel bugs are reported through fuzzers, static analyzers, mailing lists, and increasingly AI-assisted tools. These 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.
We are developing Vega, a continuous Linux kernel bug monitoring and triage platform. Vega 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, Vega attempts to generate and execute a reproducer, filters false positives, and triages severity—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 as additional debugging context.
We would like to discuss 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?