Speaker
Description
Kernel maintainers increasingly receive bug findings from fuzzers and AI-assisted tools. While these tools expand bug-finding coverage, they also create a growing triage burden. The quality of both bug reports and patches can vary substantially: findings may be false positives, lack a usable reproducer, duplicate existing issues, or come with patches that are incomplete or incorrect.
We are developing Vega, a continuous Linux kernel bug discovery and triage system inspired by syzbot. Vega aggregates findings from multiple sources and gives maintainers a view of bugs affecting their subsystems. For each finding, it attempts to generate and execute a proof of concept, helping filter false positives and turn plausible findings into reproducible bugs. For confirmed bugs, Vega further analyzes impact and severity and can generate a draft patch as a remediation reference.
We would like to share our experience triaging over 1,000 Linux kernel bugs and discuss a practical question: which bugs are actually worth fixing? AI-assisted tools can surface many real but obscure corner cases, making it increasingly important to distinguish technically real bugs from bugs that warrant engineering attention.