Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

Researchers introduce LinuxFLBench, a fault localization benchmark for Linux kernel bugs, and demonstrate that current LLM agents struggle with this complex task, achieving only 41.6% accuracy. They propose LinuxFL+, an enhancement framework that improves accuracy by 7.2-11.2% across all tested agents, addressing a critical gap in software debugging automation.

This research addresses a fundamental challenge in software engineering: automatically identifying buggy code in large, complex systems like the Linux kernel. The Linux kernel's scale, interconnected dependencies, and limited observability create conditions where existing AI debugging methods fail, with top-performing agents achieving less than 42% accuracy at file-level localization. This gap reveals important limitations in how LLM agents approach reasoning across massive codebases with subtle fault manifestations.

The work builds on recent momentum in AI-assisted software engineering, where LLM agents have shown promise on benchmarks like SWE-bench. However, SWE-bench's curated repositories don't reflect the complexity engineers face in production systems. LinuxFLBench bridges this gap by creating a realistic benchmark from actual kernel bugs, providing the community with a more challenging evaluation standard that exposes agent weaknesses in handling diverse impact factors and sparse debugging signals.

The proposed LinuxFL+ framework demonstrates practical value by delivering consistent improvements without prohibitive computational costs, suggesting that targeted enhancements to agent reasoning pipelines can meaningfully advance debugging capabilities. For software development teams and open-source maintainers, this research indicates that LLM agents remain tools requiring careful validation rather than autonomous solutions. The modest but meaningful accuracy gains (7-11%) suggest incremental progress toward more capable debugging automation, though human oversight remains essential for kernel-level code quality assurance.

Future work likely focuses on scaling these techniques to other complex systems and improving agents' ability to reason across distributed fault causes and side effects in tightly coupled codebases.

• →Current LLM agents achieve only 41.6% accuracy in Linux kernel fault localization, significantly underperforming on real-world complexity compared to curated benchmarks
• →LinuxFLBench provides the first fault localization benchmark constructed from genuine Linux kernel bugs, offering a more realistic evaluation standard
• →The LinuxFL+ framework improves accuracy across all agents by 7.2-11.2% while maintaining computational efficiency
• →Large-scale codebases with limited observability remain a significant challenge for autonomous AI debugging systems
• →AI debugging tools require continued human validation and cannot yet replace expert engineers in critical system maintenance