AuthorsG. Fraser, A. Arcuri and P. McMinn
EditorsL. Vanneschi
TitleTest Suite Generation With Memetic Algorithms
AfilliationSoftware Engineering, Software Engineering, Software Engineering
Project(s)The Certus Centre (SFI)
Publication TypeProceedings, refereed
Year of Publication2013
Conference NameACM Genetic and Evolutionary Computation Conference (GECCO)
Place PublishedNew York, NY, USA

Genetic Algorithms have been successfully applied to the generation of unit tests for classes, and are well suited to create complex objects through sequences of method calls. However, because the neighborhood in the search space for method sequences is huge, even supposedly simple optimiza- tions on primitive variables (e.g., numbers and strings) can be ine ective or unsuccessful. To overcome this problem, we extend the global search applied in the EvoSuite test gen- eration tool with local search on the individual statements of method sequences. In contrast to previous work on local search, we also consider complex datatypes including strings and arrays. A rigorous experimental methodology has been applied to properly evaluate these new local search opera- tors. In our experiments on a set of open source classes of dif- ferent kinds (e.g., numerical applications and text process- ing), the resulting test data generation technique increased branch coverage by up to 32% on average over the normal Genetic Algorithm.

Citation KeySimula.simula.1928