Complementarity of Error Detection Techniques

We study explicit techniques for detection of safety errors, e.g., depth-first search, directed search, random walk, and bitstate hashing. We argue that it is not important to find the best technique, but to find a set of complementary techniques. To this end, we choose nine diverse error detection techniques and perform experiments over a large set of models. We compare speed of techniques, lengths of reported counterexamples, and also achieved model coverage. The results show that the studied set of techniques is indeed complementary in several ways.