Test Suite Generation with Memetic Algorithms

by Gordon Fraser, Andrea Arcuri, and Phil McMinn

Genetic and Evolutionary Computation Conference (GECCO 2013)


A more recent and expanded journal version of this paper is available — see "A Memetic Algorithm for Whole Test Suite Generation".


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 optimizations on primitive variables (e.g., numbers and strings) can be ineffective or unsuccessful. To overcome this problem, we extend the global search applied in the EvoSuite test generation 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 ... [more]


Reference

Gordon Fraser, Andrea Arcuri, and Phil McMinn. Test Suite Generation with Memetic Algorithms. Genetic and Evolutionary Computation Conference (GECCO 2013), pp. 1437–1444, 2013


Bibtex Entry
@inproceedings{Fraser2013,
  author    = "Fraser, Gordon and Arcuri, Andrea and McMinn, Phil",
  title     = "Test Suite Generation with Memetic Algorithms",
  booktitle = "Genetic and Evolutionary Computation Conference (GECCO 2013)",
  pages     = "1437--1444",
  year      = "2013",
  publisher = "ACM"
}