A probabilistic analysis method for functional qualification under Mutation Analysis

Abstract

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.