Mutation Analysis (MA) is a fault-based simulation technique that is used to measure the quality of testbenches for mutant detections where mutants are simple syntactical changes in the designs. A mutant is said living if its error effect cannot be observed at the primary outputs. Previous works mainly focused on the cost reduction in the process of MA, because the MA is a computation intensive process in the commercial tool. For the living mutants, to the best of our knowledge, the commercial tool has not addressed the pattern generation issue yet. Thus, this paper presents a Genetic Algorithm to generate patterns for detecting living mutants such that the quality of the verification environment is improved. The experimental results show that more living mutants can be detected after adding the generated patterns in the testbench.

Mutation Analysis (MA) is a fault-based simulation technique that is used to measure the quality of testbenches in error (mutant) detection. Although MA effectively reports the living mutants to designers, it suffers from the high simulation cost. This paper presents a probabilistic MA preprocessing technique, Error Propagation Analysis (EPA), to speed up the MA process. EPA can statically estimate the probability of the error propagation with respect to each mutant for guiding the observation-point insertion. The inserted observation-points will reveal a mutant's status earlier during the simulation such that some useless testcases can be discarded later. We use the mutant model from an industrial EDA tool, Certitude, to conduct our experiments on the OpenCores' RT-level designs. The experimental results show that the EPA approach can save about 14% CPU time while obtaining the same mutant status report as the traditional MA approach.