Multi-Task Optimization-Based Test Data Generation for Mutation Testing via Relevance of Mutant Branch and Input Variable

Mutation testing is a powerful software testing technique. However, it is difficult to obtain test data for killing a large number of mutants, especially for hard-to-kill mutants. Mutant branch is formed by an original statement of a program under test and its mutated statement. The true branch of a mutant branch is covered by a test datum, suggesting the corresponding mutant is killed under the criterion of weak mutation testing. This article focuses on efficiently generating test data for a large number of mutant branches. When generating test data using a search-based method, the size of the search domain is a determining factor affecting the search performance. The key observation is that only partial input variables affect whether a mutant will be killed, so the search domain can be reduced by deleting irrelevant variables. Along this line, we first group the mutant branches based on their relevant input variables, followed by formulating a multi-task optimization model of test data generation for the grouped mutant branches, in which the relevant input variables are taken as the decision variables. Finally, a multi-population genetic algorithm with individual sharing is employed to generate test data by multi-tasking. The experiments based on eight programs of various sizes show that removing related variable helps reduce the search domain, and the efficiency of test data generation by grouping and multitasking is improved.