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Elastic Collisions: Case Study

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Related Experiment Videos

AutoKFL: Linux Kernel Fault Localization via ReAct-Based Multi-Agent Framework with Dynamic Crash Reproduction.

JungWoo Park1, Minju Kang2, Seungho Jeon3

  • 1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

AutoKFL enhances fault localization (FL) for Linux kernel crashes using cooperating large language model agents. This automated system improves bug identification accuracy in complex software systems.

Keywords:
Linux kernelfault localizationlarge language modelmulti-agent system

Related Experiment Videos

Area of Science:

  • Software Engineering
  • Computer Systems
  • Artificial Intelligence

Background:

  • Fault localization (FL) is crucial for bug fixing in large-scale software like the Linux kernel.
  • Challenges include vast codebases, limited runtime visibility, and complex fault causes.
  • Existing methods struggle with the scale and complexity of kernel fault analysis.

Purpose of the Study:

  • To develop an automated fault localization system for Linux kernel crashes.
  • To address the challenges of scale, visibility, and complexity in kernel FL.
  • To improve the accuracy and efficiency of identifying bug-inducing code.

Main Methods:

  • Proposed AutoKFL, an automated FL system using four cooperating large language model (LLM)-based agents.
  • Agents perform sequential tasks: crash observation, code collection, code analysis, and evidence synthesis.
  • Agents operate in a reasoning-acting (ReAct) manner with iterative exploration via conditional routing.

Main Results:

  • Evaluated on 208 Linux kernel crashes from Syzbot.
  • Achieved a file-level Recall@1 of 0.77 and a mean reciprocal rank (MRR) of 0.822.
  • Outperformed single-LLM-call approaches in both file-level and function-level localization.

Conclusions:

  • AutoKFL effectively automates fault localization for Linux kernel crashes.
  • The multi-agent, iterative approach surpasses single-agent LLM methods.
  • AutoKFL offers a promising solution for complex software fault analysis.