Library-Aware Doubles and Iterative Repair for Large Language Model-Generated Unit Tests in OpenSIL Firmware
arXiv:2606.19725v1 Announce Type: cross Abstract: Validating changes in low-level C firmware is expensive because unit tests (UTs) are fragile under strict build constraints, where missing headers, unresolved symbols, and dependency mismatches frequently prevent compilation and linking. This study introduces an automated UT authoring workflow for the Open-Source Silicon Initialization Library (openSIL) firmware codebase maintained by Advanced Micro Devices (AMD) that reduces manual effort through a large language model (LLM) guided multi-agent pipeline. The workflow combines automated generati
The increasing complexity of firmware and the maturity of large language models create an imperative and opportunity for automated testing solutions.
Automated, LLM-generated unit tests can significantly accelerate firmware development and validation, impacting the efficiency and reliability of foundational silicon components.
The manual effort and cost associated with validating critical low-level firmware can be substantially reduced through AI-driven tooling.
- · AMD
- · Semiconductor companies
- · Open-source firmware developers
- · AI software tool developers
- · Manual test engineers
- · Traditional QA methodologies
Faster development cycles and higher quality for critical low-level firmware become achievable.
The cost and time to bring new silicon designs to market could be optimized, leading to more rapid innovation.
Increased reliance on AI for foundational code validation might introduce new security vulnerabilities or require novel verification strategies for AI-generated code.
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Read at arXiv cs.AI