Equivalent Mutants Detection Based on Weighted Software Behavior Graph

The equivalent mutants problem is one of the crucial problems in mutation testing. In consequence of its existence, the effectiveness of mutation testing is underestimated. In addition, it will produce a certain amount of useless overhead. Equivalent mutants cannot be detected by any test input. The existing works mostly focus on static analysis to detect, or avoid generating, the equivalent mutants. The essence of these methods is to use prior knowledge to establish some rules of program equivalence. However, (1) it needs a lot of professional labor to sort out the equivalence rules, and (2) only a small part of the rules can be determined in advance, because of the diversity of mutation operators and mutation targets. Consequently, the best result reported so far is 50% of the equivalent mutants can be detected. Since it is generally believed that manual judgment of program equivalence is the most reliable, this paper proposes a novel method to automatically detect equivalent mutants by tracing program behavior like the professionals. The weighted software behavior graph is utilized in the detection of equivalent mutants for the first time. This method can not only figure out different execution paths, but also be sensitive to execution frequency. By comparing the weighted software behavior graphs of an alive mutant and its original program, we are able to examine more precisely whether the alive mutant is the same as the original program, in terms of the state of infection and/or the propagation. Evaluation results on an open dataset of manually evaluated equivalent mutants show that our approach can detect 77.5% of all the equivalent mutants, which is much higher than the existing static methods.