Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?

by Sina Shamshiri, José Miguel Rojas, Gordon Fraser and Phil McMinn

Genetic and Evolutionary Computation Conference (GECCO 2015)



Achieving high structural coverage is an important aim in software testing. Several search-based techniques have proved successful at automatically generating tests that achieve high coverage. However, despite the well-established arguments behind using evolutionary search algorithms (e.g., genetic algorithms) in preference to random search, it remains an open question whether the benefits can actually be observed in practice when generating unit test suites for object-oriented classes. In this paper, we report an empirical study on the effects of using a genetic algorithm (GA) to generate test suites over generating test suites incrementally with random search, by applying the EvoSuite unit test ... [more]


Reference

Sina Shamshiri, José Miguel Rojas, Gordon Fraser and Phil McMinn. Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 1367–1374, 2015


Bibtex Entry
@inproceedings{Shamshiri2015,
  author    = "Shamshiri, Sina and Rojas, Jos\'{e} Miguel and Fraser, Gordon and McMinn, Phil",
  title     = "Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?",
  booktitle = "Genetic and Evolutionary Computation Conference (GECCO 2015)",
  pages     = "1367--1374",
  year      = "2015",
  publisher = "ACM"
}