Continuous Test Generation on Guava

  • José Campos
  • Gordon Fraser
  • Andrea Arcuri
  • Rui Abreu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9275)

Abstract

Search-based testing can be applied to automatically generate unit tests that achieve high levels of code coverage on object-oriented classes. However, test generation takes time, in particular if projects consist of many classes, like in the case of the Guava library. To allow search-based test generation to scale up and to integrate it better into software development, continuous test generation applies test generation incrementally during continuous integration. In this paper, we report on the application of continuous test generation with EvoSuite at the SSBSE’15 challenge on the Guava library. Our results show that continuous test generation reduces the time spent on automated test generation by 96 %, while increasing code coverage by 13.9 % on average.

Keywords

Search-based testing Automated unit test generation Continuous integration Continuous test generation 

References

  1. 1.
    Campos, J., Arcuri, A., Fraser, G., Abreu, R.: Continuous test generation: enhancing continuous integration with automated test generation. In: IEEE/ACM International Conference on Automated Software Engineering (ASE), ASE 2014. pp. 55–66. ACM, New York (2014)Google Scholar
  2. 2.
    Fraser, G., Arcuri, A.: EvoSuite: automatic test suite generation for object-oriented software. In: ACM SIGSOFT European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), ESEC/FSE 2011, pp. 416–419. ACM, New York (2011)Google Scholar
  3. 3.
    Fraser, G., Arcuri, A.: 1600 faults in 100 projects: automatically finding faults while achieving high coverage with EvoSuite. Empirical Softw. Eng. (EMSE) 20(3), 611–639 (2013)CrossRefGoogle Scholar
  4. 4.
    Fraser, G., Arcuri, A.: A large-scale evaluation of automated unit test generation using EvoSuite. ACM Trans. Softw. Eng. Methodol. (TOSEM) 24(2), 8:1–8:42 (2014)CrossRefGoogle Scholar
  5. 5.
    Fraser, G., Arcuri, A.: Automated test generation for java generics. In: Winkler, D., Biffl, S., Bergsmann, J. (eds.) SWQD 2014. LNBIP, vol. 166, pp. 185–198. Springer, Heidelberg (2014) Google Scholar
  6. 6.
    Graves, T.L., Karr, A.F., Marron, J.S., Siy, H.: Predicting fault incidence using software change history. IEEE Trans. Softw. Eng. (TSE) 26(7), 653–661 (2000)CrossRefGoogle Scholar
  7. 7.
    Santelices, R., Chittimalli, P.K., Apiwattanapong, T., Orso, A., Harrold, M.J.: Test-suite augmentation for evolving software. In: IEEE/ACM International Conference on Automated Software Engineering (ASE), ASE 2008, pp. 218–227. IEEE Computer Society, Washington, DC (2008)Google Scholar
  8. 8.
    Xu, Z., Kim, Y., Kim, M., Rothermel, G., Cohen, M.B.: Directed test suite augmentation: techniques and tradeoffs. In: ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE), FSE 2010, pp. 257–266. ACM, New York (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José Campos
    • 1
  • Gordon Fraser
    • 1
  • Andrea Arcuri
    • 2
    • 3
  • Rui Abreu
    • 4
    • 5
  1. 1.Department of Computer ScienceThe University of SheffieldSheffieldUK
  2. 2.ScientaOsloNorway
  3. 3.University of LuxembourgLuxembourg CityLuxembourg
  4. 4.PARCPalo AltoUSA
  5. 5.University of PortoPortoPortugal

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