LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns

2026-01-06

AI Summary

This work addresses the inefficiency and error-proneness of expert-dependent unit test generation and fault debugging in hardware design. To overcome these limitations, we propose LAUDE, a novel framework that, for the first time, integrates the semantic reasoning capabilities of large language models with execution feedback mechanisms. By leveraging chain-of-thought reasoning and prompt engineering, LAUDE enables end-to-end automated test generation and fault localization with repair suggestions. The framework uniformly handles both combinational and sequential circuits, achieving defect detection rates of 100% and 93%, and debugging success rates of 93% and 84%, respectively, on the VerilogEval benchmark. These results demonstrate a significant advancement in the automation of hardware verification.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, which is often a painstaking and intense process. In this work, we introduce LAUDE, a unified unit-test generation and debugging framework for hardware designs that cross-pollinates the semantic understanding of the design source code with the Chain-of-Thought (CoT) reasoning capabilities of foundational Large-Language Models (LLMs). LAUDE integrates prompt engineering and design execution information to enhance its unit test generation accuracy and code debuggability. We apply LAUDE with closed- and open-source LLMs to a large corpus of buggy hardware design codes derived from the VerilogEval dataset, where generated unit tests detected bugs in up to 100% and 93% of combinational and sequential designs and debugged up to 93% and 84% of combinational and sequential designs, respectively.

Problem

• unit test generation
• hardware debugging
• design verification
• functional correctness
• bug localization

Innovation

• LLM-assisted debugging
• unit test generation
• hardware design verification
• Chain-of-Thought reasoning
• prompt engineering