A key problem in constrained random verification (CRV) concerns generation of input stimuli that result in good coverage of the system’s runs in targeted corners of its behavior space. Existing CRV solutions however provide no formal guarantees on the distribution of the system’s runs. In this paper, we take a first step towards solving this problem. We present an algorithm based on Algebraic Decision Diagrams for sampling bounded traces (i.e. sequences of states) of a sequential circuit with provable uniformity (or bias) guarantees, while satisfying given constraints. We have implemented our algorithm in a tool called TraceSampler. Extensive experiments show that TraceSampler outperforms alternative approaches that provide similar uniformity guarantees.

生成讲解博客的中文版本

生成AI智能校验的中文版本