Strong mutation-based test data generation using hill climbing

Mutation Testing is an effective test criterion for finding faults and assessing the quality of a test suite. Every test criterion requires the generation of test cases, which turns to be a manual and difficult task. In literature, search-based techniques are effective in generating structural-based test data. This fact motivates their use for mutation testing. Thus, if automatic test data generation can achieve an acceptable level of mutation score, it has the potential to greatly reduce the involved manual effort. This paper proposes an automated test generation approach, using hill climbing, for strong mutation. It incremental aims at strongly killing mutants, by focusing on mutants' propagation, i.e., how to kill mutants that are weakly killed but not strongly. Furthermore, the paper reports empirical results regarding the cost and effectiveness of the proposed approach on a set of 18 C programs. Overall, for the majority of the studied programs, the proposed approach achieved a higher strong mutation score than random-testing, by 19,02% on average, and the previously proposed test generation techniques that ignore mutants' propagation, by 7,2% on average. Our results also demonstrate the improved efficiency of the proposed scheme over the previous methods.