A Model for Predicting Statement Mutation Scores

Abstract

A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12.