Spec2Cov: An Agentic Framework for Code Coverage Closure of Digital Hardware Designs
Sean Lowe, Elias Hilaneh, Alma Babbit, Nakul Gopalan, Vidya Chhabria, Aman Arora

TL;DR
Spec2Cov is an innovative agentic framework that leverages large language models to automate and accelerate the process of coverage closure in hardware verification, reducing manual effort and improving efficiency.
Contribution
The paper introduces Spec2Cov, a novel framework that autonomously generates test stimuli from specifications using LLMs, coordinating with hardware simulators for coverage closure.
Findings
Achieves 100% coverage on simple designs
Reaches up to 49% coverage on complex designs
Demonstrates effectiveness across 26 diverse hardware designs
Abstract
Hardware verification is one of the most challenging stages of the hardware design process, requiring significant time and resources to ensure a design is fully validated and production-ready. Verification teams aim to maximize design coverage while ensuring correct behavior and alignment with the specification. Coverage closure, which relies on iterative constrained-random and directed testing, is still largely manual and therefore slow and labor-intensive. Recent advances show that the code generation capabilities of Large Language Models (LLMs) can be integrated with external tools to build agentic workflows that autonomously perform hardware design and verification tasks. In this work, we introduce Spec2Cov, an agentic framework that automatically and iteratively generates test stimulus directly from design specifications to accelerate coverage closure. Spec2Cov coordinates…
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