The current trend in mutation testing is to reduce the great testing effort that it involves, but it should be based on well-studied cost reduction techniques. Evolutionary Mutation Testing (EMT) aims at generating a reduced set of mutants by means of an evolutionary algorithm, which searches for potentially equivalent and difficult to kill mutants to help improve the test suite. However, there is little evidence of its applicability to other contexts beyond WS-BPEL compositions. This study explores its performance when applied to C++ object-oriented programs thanks to a newly developed system, GiGAn. The conducted experiments reveal that EMT shows stable behavior in all the case studies, where the best results are obtained when a low percentage of the mutants is generated. They also support previous studies of EMT when compared to random mutant selection, reinforcing its use for the goal of improving the fault detection capability of the test suite.
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