A machine learning approach for classification of equivalent mutants

Abstract

AbstractMutation testing is a fault‐based technique to test the quality of test suites by inducing artificial syntactic faults or mutants in a source program. However, some mutants have the same semantics as original program and cannot be detected by any test suite input known as equivalent mutants. Equivalent mutant problem (EMP) is undecidable as it requires manual human effort to identify a mutant as equivalent or killable. The constraint‐based testing (CBT) theory suggests the use of mathematical constraints which can help reveal some equivalent mutants using mutant features. In this paper, we consider three metrics of CBT theory, ie, reachability, necessity, and sufficiency to extract feature constraints from mutant programs. Constraints are extracted using program dependency graphs. Other features such as degree of significance, semantic distance, and information entropy of mutants are also extracted to build a binary classification model. Machine learning algorithms such as Random Forest, GBT, and SVM are applied under two application scenarios (split‐project and cross‐project) on ten Java programs to predict equivalent mutants. The analysis of the study demonstrates that that the proposed techniques not only improves the efficiency of the equivalent mutant detection but also reduces the effort required to perform it with small accuracy loss.