Static detection of equivalent mutants in real-time model-based mutation testing An Empirical Evaluation

Model-based mutation testing has the potential to effectively drive test generation to reveal faults in software systems. However, it faces a typical efficiency issue since it could produce many mutants that are equivalent to the original system model, making it impossible to generate test cases from them. We consider this problem when model-based mutation testing is applied to real-time system product lines, represented as timed automata. We define novel, time-specific mutation operators and formulate the equivalent mutant problem in the frame of timed refinement relations. Further, we study in which cases a mutation yields an equivalent mutant. Our theoretical results provide guidance to system engineers, allowing them to eliminate mutations from which no test case can be produced. Our empirical evaluation, based on a proof-of-concept implementation and a set of benchmarks from the literature, confirms the validity of our theory and demonstrates that in general our approach can avoid the generation of a significant amount of the equivalent mutants.