Skip to main content
SpringerLink
Log in

Multi-objective Regression Test Suite Minimisation for Mockito

  • Conference paper
  • First Online:
Search Based Software Engineering (SSBSE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9962))

Included in the following conference series:

Abstract

Regression testing is applied after modifications are performed to large software systems in order to verify that the changes made do not unintentionally disrupt other existing components. When employing regression testing it is often desirable to reduce the number of test cases executed in order to achieve a certain objective; a process known as test suite minimisation. We use multi-objective optimisation to analyse the trade-off between code coverage and execution time for the test suite of Mockito, a popular framework used to create mock objects for unit tests in Java. We show that a large reduction can be made in terms of execution time at the expense of only a small reduction in code coverage and discuss how the described methods can be easily applied to many projects that utilise regression testing.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Notes

  1. 1.

    Running on an Intel \(\copyright \) CoreTM i7-4600U CPU @ 2.10 GHz x 2.

References

  1. Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol (1997)

    Book  MATH  Google Scholar 

  2. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16. John Wiley & Sons, Hoboken (2001)

    MATH  Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  4. Epitropakis, M.G., Yoo, S., Harman, M., Burke, E.K.: Empirical evaluation of Pareto efficient multi-objective regression test case prioritisation. In: Proceedings of the International Symposium on Software Testing and Analysis (2015)

    Google Scholar 

  5. Gu, Q., Tang, B., Chen, D.: Optimal regression testing based on selective coverage of test requirements. In: Proceedings of the Parallel and Distributed Processing with Applications (2010)

    Google Scholar 

  6. Harman, M., Burke, E., Clark, J.A., Yao, X.: Dynamic adaptive search based software engineering. In: Proceedings of the Empirical Software Engineering and Measurement (2012)

    Google Scholar 

  7. Harman, M., Mansouri, S.A., Zhang, Y.: Search-based software engineering: trends, techniques and applications. ACM Comput. Surv. 45(1), 11 (2012)

    Article  Google Scholar 

  8. Yoo, S., Harman, M.: Pareto efficient multi-objective test case selection. In: Proceedings of the International Symposium on Software Testing and Analysis (2007)

    Google Scholar 

  9. Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verification Reliab. 22(2), 67–120 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics, University of York, York, YO10 5DD, UK

    Andrew J. Turner

  2. CREST, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    David R. White

  3. The OR Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    John H. Drake

Authors
  1. Andrew J. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David R. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John H. Drake
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew J. Turner .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    Federica Sarro

  2. Michigan State University , East Lansing, Michigan, USA

    Kalyanmoy Deb

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Turner, A.J., White, D.R., Drake, J.H. (2016). Multi-objective Regression Test Suite Minimisation for Mockito. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47106-8_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47105-1

  • Online ISBN: 978-3-319-47106-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation