{"ID":2842097,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10007","arxiv_id":"2511.10007","title":"AssertMiner: Module-Level Spec Generation and Assertion Mining using Static Analysis Guided LLMs","abstract":"Assertion-based verification (ABV) is a key approach to checking whether a logic design complies with its architectural specifications. Existing assertion generation methods based on design specifications typically produce only top-level assertions, overlooking verification needs on the implementation details in the modules at the micro-architectural level, where design errors occur more frequently. To address this limitation, we present AssertMiner, a module-level assertion generation framework that leverages static information generated from abstract syntax tree (AST) to assist LLMs in mining assertions. Specifically, it performs AST-based structural extraction to derive the module call graph, I/O table, and dataflow graph, guiding the LLM to generate module-level specifications and mine module-level assertions. Our evaluation demonstrates that AssertMiner outperforms existing methods such as AssertLLM and Spec2Assertion in generating high-quality assertions for modules. When integrated with these methods, AssertMiner can enhance the structural coverage and significantly improve the error detection capability, enabling a more comprehensive and efficient verification process.","short_abstract":"Assertion-based verification (ABV) is a key approach to checking whether a logic design complies with its architectural specifications. Existing assertion generation methods based on design specifications typically produce only top-level assertions, overlooking verification needs on the implementation details in the mo...","url_abs":"https://arxiv.org/abs/2511.10007","url_pdf":"https://arxiv.org/pdf/2511.10007v1","authors":"[\"Hongqin Lyu\",\"Yonghao Wang\",\"Jiaxin Zhou\",\"Zhiteng Chao\",\"Tiancheng Wang\",\"Huawei Li\"]","published":"2025-11-13T06:23:28Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Large Language Model\"]","has_code":false}