Speakers
Description
ABSTRACT
The lack of standardized documentation for the Linux kernel poses a
barrier to its adoption in safety-critical industries such as aerospace,
where compliance with standards like DO-178C is required. We explored
the use of locally trained Large Language Models (LLMs) to automatically
generate compliant documentation for kernel modules and tools. As a case
study, we applied this approach to the Linux kernel’s ftrace utility and
evaluated four LLM families—Meta Llama, StarCoder2, Mistral Devstral,
and Google Gemma—across documentation validity, Graphical Processing
Unit (GPU) utilization, and throughput. Results show that Mistral Devstral
produced the most accurate and standards-aligned documentation and
demonstrated that LLMs can provide an effective method for bridging the
gap between open-source software and regulated environments, enabling
safer and broader integration of the Linux kernel into aerospace and other
compliance-driven domains.
Reason Behind Effort
The broader goal is not to propose an immediate solution, but to present empirical results that raise questions for the community: What criteria should kernel-generated documentation meet? Can LLMs be integrated into existing toolchains (e.g., Sphinx, SPDX) to support compliance goals? What processes would allow reproducibility, traceability, and expert validation in a way that certification authorities might accept?