Set evolution based test data generation for killing stubborn mutants

Abstract

Mutation testing is a fault-based and powerful software testing technique, but the large number of mutations can result in extremely high costs. To reduce the cost of mutation testing, researchers attempt to identify stubborn mutants and generate test data to kill them, in order to achieve the same testing effect. However, existing methods suffer from inaccurate identification of stubborn mutants and low productiveness in generating test data, which will seriously affect the effectiveness and efficiency of mutation testing. Therefore, we propose a new method of generating test data for killing stubborn mutants based on set evolution, namely TDGMSE. We first propose an integrated indicator to identify stubborn mutants. Then, we establish a constrained multi-objective model for generating test data of killing stubborn mutants. Finally, we develop a new genetic algorithm based on set evolution to solve the mathematical model. The results on 14 programs depict that the average false positive (or negative) rate of TDGMSE is decreased about 81.87% (or 32.34%); the success rate of TDGMSE is 99.22%; and the average number of iterations of TDGMSE is 16132.23, which is lowest of all methods. The research highlights several potential research directions for mutation testing.