DeepVerifier: Learning to Update Test Sequences for Coverage-Guided Verification
Yuntao Lu, Chen Bai, Yuxuan Zhao, Ziyue Zheng, Yangdi Lyu, Mingyu Liu, Bei Yu · ACM Transactions on Design Automation of Electronic Systems · 2025
Verification is critical in ensuring the reliable operation of modern, complex computing systems. However, as processor designs become increasingly sophisticated, conventional static verification techniques struggle to generate high-quality test sequences that achieve comprehensive coverage. Dynamic simulation-based approaches, which leverage coverage-driven objectives, can increase confidence in correct processor functionality but often suffer from low verification efficiency due to the generation of redundant test sequences and significant computational overhead. To address these challenges, this paper presents DeepVerifier, a novel coverage-guided test generation framework that leverages data-driven learning of existing test sequences and their associated coverage feedback. DeepVerifier uses a language model to learn the semantic representations of test sequences, ensure adherence to syntax constraints, and estimate the relationship between test sequences and coverage scores. By updating test sequences with higher coverage, DeepVerifier can significantly improve the efficiency and effectiveness of the verification process. Experimental results of verifying an out-of-order RISC-V microprocessor demonstrate that the framework accurately estimates the coverage scores of test sequences and updates high-quality sequences that contribute to higher coverage. This coverage-guided test generation technique holds promise for enhancing the reliability of modern processor designs.