Documentation-Driven Compiler Fuzzing with Large Language Models

2025-03-26

Summary: A sourced summary of an experiment where LLM agents used documentation and a compiler binary to find real compiler issues.

This sourced technical post describes a weekend experiment using LLMs as black-box fuzzing agents for a compiler. Instead of reading source code, the agents used documentation, generated test programs, ran them, and looked for crashes, inconsistencies, or documentation mismatches.

The experiment reportedly found 10 meaningful compiler issues with about $80 of OpenAI API usage. The broader lesson is that documentation-driven testing can uncover bugs because AI agents can behave like many curious developers trying different combinations of language features.

Free Basics version: good documentation is not only for users. It can also help AI tools test software and find problems.

Source: Daniil Sedov, Gusarich's thoughts, gusarich.com/blog/fuzzing-with-llms