A Multi-objective optimization approach for selection of second order mutant generation strategies

Abstract

The use of Higher-Order Mutants (HOMs) presents some advantages concerning the traditional use of First-Order Mutants (FOMs). HOMs can better simulate real and subtle faults, reduce the number of generated mutants and test cases, and so on. However, the HOM space is potentially huge, and an efficient strategy to generate the best HOMs is fundamental. In the literature different strategies were proposed and evaluated, mainly to generate Second-Order Mutants (SOMs), but none has been proved to perform better in different situations. Due to this, the selection of the best strategy is an important task. Most times a lot of experiments need to be conducted. To help the tester in this task and to allow the use of HOMs in practice, this paper proposes a hyper-heuristic approach. Such approach is based on NSGA-II and uses the selection method Choice Function to automatically choose among different Low-Level Heuristics (LLHs), which, in this case, are search-operators related to existing SOM generation strategies. The performance of each LLH is related to some objectives such as the number of SOMs generated, the capacity to capture subtler faults and replace the constituent FOMs. In comparison with existing strategies, our approach obtained better results considering the used objectives, and statistically equivalent results considering mutation score with respect to the FOMs.