High quality behavioral verification using statistical stopping criteria

In order to improve the efficiency of behavioral model verification, it is important to determine the points of diminishing return for a given verification strategy. This paper compares the existing stopping rules and presents a new stopping rule based on static Bayesian technique. The new stopping rule was applied to verifying 14 complex VHDL models. We used the figure of merit to compare the efficiency of the stopping rules. The results in terms of coverage and verification time were shown to consistently outperform existing stopping rules