Skip to main content
SpringerLink
Log in

Multi-objective Genetic Improvement: A Case Study with EvoSuite

  • Conference paper
  • First Online:
Search-Based Software Engineering (SSBSE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13711))

Included in the following conference series:

Abstract

Automated multi-objective software optimisation offers an attractive solution to software developers wanting to balance often conflicting objectives, such as memory consumption and execution time. Work on using multi-objective search-based approaches to optimise for such non-functional software behaviour has so far been scarce, with tooling unavailable for use. To fill this gap we extended an existing generalist, open source, genetic improvement tool, Gin, with a multi-objective search strategy, NSGA-II. We ran our implementation on a mature, large software to show its use. In particular, we chose EvoSuite—a tool for automatic test case generation for Java. We use our multi-objective extension of Gin to improve both the execution time and memory usage of EvoSuite. We find improvements to execution time of up to 77.8% and improvements to memory of up to 9.2% on our test set. We also release our code, providing the first open source multi-objective genetic improvement tooling for improvement of memory and runtime for Java.

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 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
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.

    A pull request can be found at https://github.com/gintool/gin/pull/89.

References

  1. https://github.com/SOLAR-group/EvoSuiteGI

  2. Basios, M., Li, L., Wu, F., Kanthan, L., Barr, E.T.: Darwinian data structure selection. In: ESEC/SIGSOFT FSE, pp. 118–128. ACM (2018)

    Google Scholar 

  3. Blot, A., Petke, J.: Empirical comparison of search heuristics for genetic improvement of software. IEEE TEVC 25(5), 1001–1011 (2021)

    Google Scholar 

  4. Brownlee, A.E.I., Petke, J., Alexander, B., Barr, E.T., Wagner, M., White, D.R.: Gin: genetic improvement research made easy. In: Auger, A., Stützle, T. (eds.) GECCO, pp. 985–993. ACM (2019)

    Google Scholar 

  5. Bruce, B.R., Petke, J., Harman, M., Barr, E.T.: Approximate oracles and synergy in software energy search spaces. IEEE TSE 45(11), 1150–1169 (2019)

    Google Scholar 

  6. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83

    Chapter  Google Scholar 

  7. Fraser, G., Arcuri, A.: EvoSuite: automatic test suite generation for object-oriented software. In: SIGSOFT FSE, pp. 416–419. ACM (2011)

    Google Scholar 

  8. Hort, M., Kechagia, M., Sarro, F., Harman, M.: A survey of performance optimization for mobile applications. IEEE TSE 48(8), 2879–2904 (2022)

    Google Scholar 

  9. Jin, G., Song, L., Shi, X., Scherpelz, J., Lu, S.: Understanding and detecting real-world performance bugs. In: PLDI, PLDI 2012, pp. 77–88 (2012)

    Google Scholar 

  10. Motwani, M., Soto, M., Brun, Y., Just, R., Goues, C.L.: Quality of automated program repair on real-world defects. IEEE TSE 48(2), 637–661 (2022)

    Google Scholar 

  11. Petke, J., Haraldsson, S.O., Harman, M., Langdon, W.B., White, D.R., Woodward, J.R.: Genetic improvement of software: a comprehensive survey. IEEE TEVC 22(3), 415–432 (2018)

    Google Scholar 

  12. White, D.R., Arcuri, A., Clark, J.A.: Evolutionary improvement of programs. IEEE TEVC 15(4), 515–538 (2011)

    Google Scholar 

  13. Zuo, S., Blot, A., Petke, J.: Evaluation of genetic improvement tools for improvement of non-functional properties of software. In: Fieldsend, J.E., Wagner, M. (eds.) GECCO 2022, pp. 1956–1965. ACM (2022)

    Google Scholar 

Download references

Acknowlegements

This work was funded by the EPSRC grant EP/P023991/1.

Author information

Authors and Affiliations

  1. University College London, London, UK

    James Callan & Justyna Petke

Authors
  1. James Callan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justyna Petke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James Callan .

Editor information

Editors and Affiliations

  1. Université du Luxembourg, Luxembourg, Luxembourg

    Mike Papadakis

  2. Federal University of Paraná, Curitiba, Brazil

    Silvia Regina Vergilio

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Callan, J., Petke, J. (2022). Multi-objective Genetic Improvement: A Case Study with EvoSuite. In: Papadakis, M., Vergilio, S.R. (eds) Search-Based Software Engineering. SSBSE 2022. Lecture Notes in Computer Science, vol 13711. Springer, Cham. https://doi.org/10.1007/978-3-031-21251-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21251-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21250-5

  • Online ISBN: 978-3-031-21251-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation