Skip to main content
SpringerLink
Log in
Book cover

Advanced Computational Methods for Knowledge Engineering pp 391–402Cite as

Searching for Strongly Subsuming Higher Order Mutants by Applying Multi-objective Optimization Algorithm

  • Conference paper

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 358))

Abstract

Higher order mutation testing is considered a promising solution for overcoming the main limitations of first order mutation testing. Strongly subsuming higher order mutants (SSHOMs) are the most valuable among all kinds of higher order mutants (HOMs) generated by combining first order mutants (FOMs). They can be used to replace all of its constituent FOMs without scarifying test effectiveness. Some researchers indicated that searching for SSHOMs is a promising approach. In this paper, we not only introduce a new classification of HOMs but also new objectives and fitness function which we apply in multi-objective optimization algorithm for finding valuable SSHOMs.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. DeMillo, R.A., Lipton, R.J., Sayward, F.G.: Hints on test data selection: help for the practicing programmer. IEEE Computer 11(4), 34–41 (1978)

    Article  Google Scholar 

  2. Hamlet, R.G.: Testing programs with the aid of a compiler. IEEE Transactions on Software Engineering SE-3(4), 279–290 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  3. Jia, Y., Harman, M.: Higher order mutation testing. Information and Software Technology 51, 1379–1393 (2009)

    Article  Google Scholar 

  4. Harman, M., Jia, Y., Langdon, W.B.: A Manifesto for Higher Order Mutation Testing. In: Third International Conf. on Software Testing, Verification, and Validation Workshops (2010)

    Google Scholar 

  5. Langdon, W.B., Harman, M., Jia, Y.: Efficient multi-objective higher order mutation testing with genetic programming. The Journal of Systems and Software 83 (2010)

    Google Scholar 

  6. Jia, Y., Harman, M.: Constructing Subtle Faults Using Higher Order Mutation Testing. In: Proc. Eighth Int’l Working Conf. Source Code Analysis and Manipulation (2008)

    Google Scholar 

  7. Langdon, W.B., Harman, M., Jia, Y.: Multi Objective Higher Order Mutation Testing with Genetic Programming. In: Proc. Fourth Testing: Academic and Industrial Conf. Practice and Research (2009)

    Google Scholar 

  8. Madeyski, L.: On the effects of pair programming on thoroughness and fault-finding effectiveness of unit tests. In: Münch, J., Abrahamsson, P. (eds.) PROFES 2007. LNCS, vol. 4589, pp. 207–221. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Madeyski, L.: The impact of pair programming on thoroughness and fault detection effectiveness of unit tests suites. Wiley, Software Process: Improvement and Practice 13(3), 281–295 (2008), doi:10.1002/spip.382

    Article  Google Scholar 

  10. Madeyski, L.: The impact of test-first programming on branch coverage and mutation score indicator of unit tests: An experiment. Information and Software Technology 52(2), 169–184 (2010), doi:10.1016/j.infsof.2009.08.007

    Article  Google Scholar 

  11. Madeyski, L., Orzeszyna, W., Torkar, R., Józala, M.: Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation. IEEE Transactions on Software Engineering 40(1), 23–42 (2014), doi:10.1109/TSE.2013.44

    Article  Google Scholar 

  12. Mresa, E.S., Bottaci, L.: Efficiency of mutation operators and selective mutation strategies: An empirical study. Software Testing, Verification and Reliability (1999)

    Google Scholar 

  13. Papadakis, M., Malevris, N.: An empirical evaluation of the first and second order mutation testing strategies. In: Proceedings of the 2010 Third International Conference on Software Testing, Verification, and Validation Workshops, ICSTW 2010, pp. 90–99. IEEE Computer Society (2010)

    Google Scholar 

  14. Vincenzi, A.M.R., Nakagawa, E.Y., Maldonado, J.C., Delamaro, M.E., Romero, R.A.F.: Bayesian-learning based guide-lines to determine equivalent mutants. International Journal of Soft. Eng. and Knowledge Engineering 12(6), 675–690 (2002)

    Article  Google Scholar 

  15. Polo, M., Piattini, M., Garcia-Rodriguez, I.: Decreasing the Cost of Mutation Testing with Second-Order Mutants. Software Testing, Verification, and Reliability 19(2), 111–131 (2008)

    Article  Google Scholar 

  16. Madeyski, L., Radyk, N.: Judy - a mutation testing tool for Java. IET Software 4(1), 32–42 (2010), doi:10.1049/iet-sen.2008.0038

    Article  Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2) (April 2002)

    Google Scholar 

  18. Nguyen, Q.V., Madeyski, L.: Problems of Mutation Testing and Higher Order Mutation Testing. In: van Do, T., Le Thi, H.A., Nguyen, N.T. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 282, pp. 157–172. Springer, Heidelberg (2014), doi:10.1007/978-3-319-06569-4_12

    Chapter  Google Scholar 

  19. Papadakis, M., Yue, J., Harman, M., Traon, Y.L.: Trivial Compiler Equivalence: A Large Scale Empirical Study of a Simple, Fast and Effective Equivalent Mutant Detection Technique. In: 37th International Conference on Software Engineering, ICSE (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Quang Vu Nguyen & Lech Madeyski

Authors
  1. Quang Vu Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lech Madeyski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quang Vu Nguyen .

Editor information

Editors and Affiliations

  1. LITA - UFR MIM, University of Lorraine – Metz, Metz, France

    Hoai An Le Thi

  2. Institute of Informatics & Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  3. Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

    Tien Van Do

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Nguyen, Q.V., Madeyski, L. (2015). Searching for Strongly Subsuming Higher Order Mutants by Applying Multi-objective Optimization Algorithm. In: Le Thi, H., Nguyen, N., Do, T. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-319-17996-4_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17996-4_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17995-7

  • Online ISBN: 978-3-319-17996-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation