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Test Data Generation Techniques for Mutation Testing: A Systematic Mapping

Mutation testing is regarded as an effective testing criterion. However, its main downside is that it is an expensive and time consuming technique, which has hampered its widespread adoption in industrial settings. Automatic test data generation is an option to deal with this problem. The successful automation of this activity requires the generation of a small number of tests that can achieve good mutation score. In this paper, we describe the process, results and discussions of a systematic mapping conducted to gather and identify evidence with respect to the techniques and approaches for test data generation in mutation testing. We selected 19 primary studies that focus on techniques based in search, code coverage, restrictions, and hybrid techniques. Our results indicate that most of the reported approaches are not industry-ready, which underscores the importance of further research in the area so that automatic test data generation can reach an acceptable level of maturity and be used in realistic settings.