UVMarvel automatically constructs subsystem-level UVM testbenches for mainstream bus protocols using LLMs, an IR, and supporting libraries, reaching 95.65% average code coverage in 4.5 hours of automated runtime.
Deepassert: An llm-aided verification framework with fine-grained assertion generation for modules with extracted module specifications
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VLP adds an NL documentation layer with trace-linked mismatch detection and derived formal checks to make human validation of LLM code feasible, lifting pass@1 from 28.7-73.2% to 65.4-93.5%.
Introduces a quality-aware loop with mutation-guided refinement, RTL-aware solver selection, and causal narrative synthesis to improve LLM-generated assertions for formal verification of RTL designs.
CoverAssert iteratively improves LLM-generated assertions via syntax-semantic clustering and coverage feedback, yielding 9.57% branch, 9.64% statement, and 15.69% toggle coverage gains on four open-source designs when combined with prior tools.
citing papers explorer
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UVMarvel: an Automated LLM-aided UVM Machine for Subsystem-level RTL Verification
UVMarvel automatically constructs subsystem-level UVM testbenches for mainstream bus protocols using LLMs, an IR, and supporting libraries, reaching 95.65% average code coverage in 4.5 hours of automated runtime.
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Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming
VLP adds an NL documentation layer with trace-linked mismatch detection and derived formal checks to make human validation of LLM code feasible, lifting pass@1 from 28.7-73.2% to 65.4-93.5%.
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Closing the Loop on LLM-Generated RTL Assertions with Quality-Aware Formal Verification
Introduces a quality-aware loop with mutation-guided refinement, RTL-aware solver selection, and causal narrative synthesis to improve LLM-generated assertions for formal verification of RTL designs.
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CoverAssert: Iterative LLM Assertion Generation Driven by Functional Coverage via Syntax-Semantic Representations
CoverAssert iteratively improves LLM-generated assertions via syntax-semantic clustering and coverage feedback, yielding 9.57% branch, 9.64% statement, and 15.69% toggle coverage gains on four open-source designs when combined with prior tools.
- From Indiscriminate to Targeted: Functionally Critical Signal-Driven Assertion Generation using LLMs for Efficient RTL Verification