Library-Aware Doubles and Iterative Repair for Large Language Model-Generated Unit Tests in OpenSIL Firmware

Researchers developed an LLM-guided automated workflow that generates compilable unit tests for AMD's OpenSIL firmware library, achieving 96% compilation success and up to 98.8% line coverage by combining test scaffolding, library-aware mocking, and iterative repair loops driven by build logs.

This research addresses a genuine pain point in firmware development where unit testing remains labor-intensive due to strict build constraints, missing dependencies, and symbol resolution issues. AMD's OpenSIL library represents a critical infrastructure component in silicon initialization, making robust testing essential but traditionally expensive. The study demonstrates that LLM-guided multi-agent pipelines can substantially automate test creation while maintaining quality, suggesting that AI tooling is moving beyond simple code generation into domain-specific problem-solving.

The technical approach combines three innovations: automated scaffolding generation, library-aware stub creation that understands firmware constraints, and an iterative repair mechanism that learns from compilation failures. The 73 out of 76 functions achieving compilable tests reflects practical viability rather than theoretical promise. The dramatic improvement in line coverage—from 73.9% without guidance to 98.8% with line-coverage feedback—indicates that LLMs respond effectively to structured feedback loops, a finding relevant across software development domains.

For the semiconductor and developer tools industries, this work signals that AI-assisted testing could reduce development cycles and lower quality assurance costs for complex firmware projects. AMD and other hardware manufacturers maintaining large codebases may adopt similar workflows to accelerate validation. The emphasis on vector-database retrieval augmentation suggests future integration with enterprise code repositories and domain-specific knowledge bases.

The next critical validation phase involves deploying this workflow on production codebases and measuring actual defect detection versus traditionally-authored tests. Success metrics should include not just coverage statistics but real-world bug catches and maintenance burden reduction.

• →LLM-guided test generation achieved 96% compilation success on firmware unit tests by iteratively repairing build failures.
• →Line coverage reached 98.8% when combined with coverage-guided feedback and retrieval-augmented generation techniques.
• →Library-aware stub and mock generation substantially reduces manual scaffolding work in constrained firmware environments.
• →Iterative compile-dispatch repair loops driven by build logs prove effective for handling dependency resolution in low-level C code.
• →The approach reduces manual debugging effort while maintaining test quality for critical silicon initialization codebases.