Fault Detection Context Based Equivalent Mutant Identification Algorithm

Although studied for almost forty years, the mutation testing has been prevented from being widely applied in industrial practice by the problem of equivalent mutants. To overcome the problem, a algorithm of using fault detection context to predict the equivalence of mutants is proposed. It makes use of static analysis technique to extract feature information about the program context around mutated program, which is called its fault detection context. Then the context information is translated into a document model, which describes the feature of mutant using natural language. The representation learning network is further used to encode fault context features. Finally, machine learning model is used to predict the equivalence of each mutant with respect to its fault detection context. An empirical study on 118000 mutants from 22 C programs is performed to validate the proposed method. The results show that the method achieves 91% of precision and 82% of recall in classifying mutants as equivalent, while 77% of precision and 78% of recall are achieved in cross-project validation. It implies the fault detection context based technique can dramatically improve the efficiency and effectiveness of equivalent mutants detection, which effectively facilitates the efficiency for mutation testing process.