SoK: From Finding to Deployment: Systematizing the OS Kernel Bug Lifecycle

A systematization of knowledge on the Linux kernel bug lifecycle, revealing a structural automation gradient from discovery to deployment.

Luyao bai; Gengda She; Kenan Alghythee; Hang Zhang; Xiaoguang Wang· 2026· DOI 10.48550/arXiv.2609.23218

The core problem

Automated kernel bug discovery has advanced rapidly, with continuous fuzzing and static analysis systems such as syzbot exposing Linux kernel bugs at a scale that downstream processes struggle to absorb. However, a crash report is only the beginning. Before a bug is eliminated, it must be triaged, understood, patched, validated, reviewed, integrated, and often backported. These later stages remain far less automated, creating a persistent gap between bug discovery and patch deployment. This paper presents a systematization of knowledge (SoK) that organizes prior work and production systems into five stages: discovery, triage, patch generation, patch validation, and integration. The authors explain the resulting automation gradient through kernel-specific challenges such as concurrency, implicit invariants, cross-syscall state, hardware dependence, lack of fault isolation, and architecture/configuration multiplicity. The analysis is grounded in a measurement of real syzbot-fixed bugs, showing that the crash-to-patch gap is not merely a backlog of unfixed reports but a structural failure mode of the repair pipeline.

The measurement of syzbot-fixed bugs reveals that even after being fixed, bugs often remain open for weeks, require review-driven patch revisions, or lack reproducers that current repair and validation systems assume. Key findings include: - A significant portion of fixed bugs still have a long tail in the patch lifecycle, with delays in integration and backporting. - Many bug reports lack reliable reproducers, which are often prerequisites for automated repair and validation. - Patch revisions are frequently driven by review feedback, indicating that initial patches are often incomplete or incorrect. - The crash-to-patch gap is not simply a backlog; it is a structural failure mode where the repair pipeline assumes artifacts that are missing. These results highlight a mismatch between the maturity of kernel-security automation in discovery and the actual bottlenecks in bug closure. The data suggests that closing the gap requires treating reproducers, localized root causes, and correctness oracles as outputs to be produced, not prerequisites to be assumed.

Automated kernel bug discovery has advanced rapidly, with continuous fuzzing and static analysis systems such as syzbot exposing Linux kernel bugs at a scale that downstream processes struggle to absorb. However, a crash report is only the beginning. Before a bug is eliminated, it must be triaged, understood, patched, validated, reviewed, integrated, and often backported. These later stages remain far less automated, creating a persistent gap between bug discovery and patch deployment. This paper presents a systematization of knowledge (SoK) that organizes prior work and production systems into five stages: discovery, triage, patch generation, patch validation, and integration. The authors explain the resulting automation gradient through kernel-specific challenges such as concurrency, implicit invariants, cross-syscall state, hardware dependence, lack of fault isolation, and architecture/configuration multiplicity. The analysis is grounded in a measurement of real syzbot-fixed bugs, showing that the crash-to-patch gap is not merely a backlog of unfixed reports but a structural failure mode of the repair pipeline.

The authors conduct a systematic literature review and production system analysis to construct a five-stage taxonomy of the Linux kernel bug lifecycle. They then perform a measurement study on real syzbot-fixed bugs to quantify the gap between crash discovery and patch deployment. The measurement focuses on metrics such as time from crash report to fix, number of patch revisions, and availability of reproducers. The study identifies kernel-specific challenges that contribute to the automation gradient, including:

The authors analyze the automation gradient across the five stages, explaining why later stages are less automated. They argue that kernel-specific challenges create a fundamental mismatch between the assumptions of current repair and validation techniques and the reality of kernel bug reports. For example, many techniques assume reliable reproducers, localized root causes, and checkable correctness oracles, yet these are precisely the artifacts missing from many real kernel bug reports. The discussion emphasizes that closing the crash-to-patch gap requires a paradigm shift: instead of assuming these artifacts, the community must develop methods to produce them as part of the repair process. The paper also outlines future directions, such as improving triage automation, generating reproducers from crash reports, and developing validation oracles for kernel patches. The analysis is grounded in the measurement data, which shows that the gap is not due to a lack of effort but to structural issues in the lifecycle. The authors conclude that a holistic approach, integrating advances across all five stages, is necessary to reduce the gap between bug discovery and deployment.

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