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A variability perspective of mutation analysis

Published:11 November 2014Publication History

ABSTRACT

Mutation testing is an effective technique for either improving or generating fault-finding test suites. It creates defective or incorrect program artifacts of the program under test and evaluates the ability of test suites to reveal them. Despite being effective, mutation is costly since it requires assessing the test cases with a large number of defective artifacts. Even worse, some of these artifacts are behaviourally ``equivalent'' to the original one and hence, they unnecessarily increase the testing effort. We adopt a variability perspective on mutation analysis. We model a defective artifact as a transition system with a specific feature selected and consider it as a member of a mutant family. The mutant family is encoded as a Featured Transition System, a compact formalism initially dedicated to model-checking of software product lines. We show how to evaluate a test suite against the set of all candidate defects by using mutant families. We can evaluate all the considered defects at the same time and isolate some equivalent mutants. We can also assist the test generation process and efficiently consider higher-order mutants.

References

  1. M. Acher, A. Cleve, G. Perrouin, P. Heymans, C. Vanbeneden, P. Collet, and P. Lahire. On extracting feature models from product descriptions. In VaMoS, pages 45–54, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. K. Aichernig, H. Brandl, E. Jöbstl, W. Krenn, R. Schlick, and S. Tiran. Killing strategies for model-based mutation testing. STVR, 2014.Google ScholarGoogle Scholar
  3. B. K. Aichernig, M. Weiglhofer, and F. Wotawa. Improving fault-based conformance testing. Electr. Notes Theor. Comput. Sci., 220(1):63–77, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. P. Ammann, M. E. Delamaro, and J. Offutt. Establishing theoretical minimal sets of mutants. In ICST, pages 21–30. IEEE, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. E. Ammann, P. E. Black, and W. Majurski. Using model checking to generate tests from specifications. In FEM, pages 46–54. IEEE, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. N. Bombieri, F. Fummi, and G. Pravadelli. A mutation model for the SystemC TLM 2.0 communication interfaces. In DATE, pages 396–401. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Q. Boucher, G. Perrouin, and P. Heymans. Deriving configuration interfaces from feature models: A vision paper. In VaMoS, pages 37–44, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Classen, M. Cordy, P.-Y. Schobbens, P. Heymans, A. Legay, and J.-F. Raskin. Featured transition systems: Foundations for verifying variability-intensive systems and their application to ltl model checking. IEEE TSE, 39(8):1069–1089, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Cohen, M. Dwyer, and J. Shi. Interaction testing of highly-configurable systems in the presence of constraints. In ISSTA, pages 129–139, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Cordy, A. Classen, P. Heymans, P.-Y. Schobbens, and A. Legay. Provelines: A product-line of verifiers for software product lines. In SPLC ’13 Workshops, pages 141–146. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Cordy, A. Classen, G. Perrouin, P.-Y. Schobbens, P. Heymans, and A. Legay. Simulation-based abstractions for software product-line model checking. In M. Glinz, G. C. Murphy, and M. Pezzè, editors, ICSE, pages 672–682. IEEE, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. R. A. DeMillo and A. J. Offutt. Constraint-based automatic test data generation. IEEE TSE, 17(9):900–910, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. X. Devroey, G. Perrouin, M. Cordy, P.-Y. Schobbens, A. Legay, and P. Heymans. Towards statistical prioritization for software product lines testing. In VaMoS, pages 10:1–10:7. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. X. Devroey, G. Perrouin, A. Legay, M. Cordy, P.-y. Schobbens, and P. Heymans. Coverage Criteria for Behavioural Testing of Software Product Lines. In ISoLA. Springer, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. C. P. F. Fabbri, J. C. Maldonado, P. C. Masiero, and M. E. Delamaro. Proteum/fsm: A tool to support finite state machine validation based on mutation testing. In SCCC, pages 96–104. IEEE Computer Society, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. C. P. F. Fabbri, J. C. Maldonado, T. Sugeta, and P. C. Masiero. Mutation testing applied to validate specifications based on Statecharts. In SRE, pages 210–219. IEEE, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Henard, M. Papadakis, G. Perrouin, J. Klein, and Y. Le Traon. Towards automated testing and fixing of re-engineered feature models. In ICSE, pages 1245–1248. IEEE, May 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. Henard, M. Papadakis, G. Perrouin, J. Klein, and Y. L. Traon. Multi-objective test generation for software product lines. In SPLC, pages 62–71. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Y. Jia and M. Harman. MILU: A Customizable, Runtime-Optimized Higher-Order Mutation Testing Tool for the Full C Language. In TAIC-PART, pages 94–98, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Y. Jia and M. Harman. An analysis and survey of the development of mutation testing. IEEE TSE, 37(5):649–678, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. F. Johansen, Ø. Haugen, F. Fleurey, A. G. Eldegard, and T. Syversen. Generating better partial covering arrays by modeling weights on sub-product lines. In MoDELS, pages 269–284. Springer, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. Spencer Peterson. Feature-Oriented domain analysis (FODA) feasibility study. Technical report, Soft. Eng. Inst., Carnegie Mellon Univ., 1990.Google ScholarGoogle Scholar
  23. C. Kästner, A. von Rhein, S. Erdweg, J. Pusch, S. Apel, T. Rendel, and K. Ostermann. Toward variability-aware testing. In FOSD, pages 1–8. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. H. P. Kim, S. Khurshid, and D. S. Batory. Shared execution for efficiently testing product lines. In ISSRE, pages 221–230. IEEE, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. D. Lorenzoli, L. Mariani, and M. Pezzè. Automatic generation of software behavioral models. In ICSE, pages 501–510, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. L. Madeyski, W. Orzeszyna, R. Torkar, and M. Jozala. Overcoming the equivalent mutant problem: A systematic literature review and a comparative experiment of second order mutation. IEEE TSE, 40(1):23–42, Jan 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. H. V. Nguyen, C. Kästner, and T. N. Nguyen. Exploring variability-aware execution for testing plugin-based web applications. In ICSE. IEEE, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. J. Offutt. A mutation carol: Past, present and future. Information & Software Technology, 53(10):1098––1107, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  29. G. Perrouin, S. Oster, S. Sen, J. Klein, B. Baudry, and Y. L. Traon. Pairwise testing for software product lines: comparison of two approaches. Soft. Qual. Journal, 20(3-4):605–643, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. R. H. Untch, A. J. Offutt, and M. J. Harrold. Mutation analysis using mutant schemata. In ISSTA, pages 139–148. ACM, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            • Published in

              cover image ACM Conferences
              FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering
              November 2014
              856 pages
              ISBN:9781450330565
              DOI:10.1145/2635868

              Copyright © 2014 ACM

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              Publication History

              • Published: 11 November 2014

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