Is PSO a valid option for search-based test case generation in the context of dynamically-typed languages?

Abstract

In recent decades, automatic test generation has advanced significantly, providing developers with time-saving benefits and facilitating software debugging. While most research in this field focused on search-based test generation tools for statically-typed languages, only a few have been adapted for dynamically-typed languages. The larger search-space, generated by the dynamic allocation of types, causes standard search-based algorithms to not be as efficient in this domain and requiring a different approach. Existing algorithms like NSGA-II, MOSA and DynaMOSA have been employed to address this problem, but exploring different approaches may yield better results. That is why this paper proposes a different procedure based on an adaptation of the particle swarm optimization algorithm (PSO). The adaptation was evaluated using
the SynTest framework, showing that DynaMOSA achieves better results than the presented approach, both when comparing the PSO adaptation with and without DynaMOSA features, and when comparing the base DynaMOSA algorithm with PSO adapted to include DynaMOSA ingredients.