Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Search Based Software Engineering

SSBSE 2016: Search Based Software Engineering pp 18–33Cite as

HOMI: Searching Higher Order Mutants for Software Improvement

  • Conference paper
  • First Online:

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

Abstract

This paper introduces HOMI, a Higher Order Mutation based approach for Genetic Improvement of software, in which the code modification granularity is finer than in previous work while scalability remains. HOMI applies the NSGAII algorithm to search for higher order mutants that improve the non-functional properties of a program while passing all its regression tests. Experimental results on four real-world C programs shows that up to 14.7 % improvement on time and 19.7 % on memory are found using only First Order Mutants. By combining these First Order Mutants, HOMI found further improvement in Higher Order Mutants, giving an 18.2 % improvement on the time performance while keeping the memory improvement. A further manual analysis suggests that 88 % of the mutation changes cannot be generated using line based ‘plastic surgery’ Genetic Improvement approaches.

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   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

Learn about institutional subscriptions

References

  1. Agrawal, H., DeMillo, R.A., Hathaway, B., Hsu, W., Hsu, W., Krauser, E.W., Martin, R.J., Mathur, A.P., Spafford, E.: Design of mutant operators for the C programming language. techreport SERC-TR-41-P, Purdue University, West Lafayette, Indiana, March 1989

    Google Scholar 

  2. Arcuri, A., Briand, L.: A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Softw. Test. Verif. Reliab. 24(3), 219–250 (2014)

    Article  Google Scholar 

  3. Arcuri, A., Fraser, G.: On parameter tuning in search based software engineering. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 33–47. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Barr, E.T., Brun, Y., Devanbu, P., Harman, M., Sarro, F.: The plastic surgery hypothesis. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2014, pp. 306–317. ACM, New York (2014)

    Google Scholar 

  5. Berger, E.D., Zorn, B.G.: Diehard: probabilistic memory safety for unsafe languages. In: Programming Language Design and Implementation, PLDI 2006 (2006)

    Google Scholar 

  6. Brake, N., Cordy, J.R., Dan, Y.E., Litoiu, M., Popes U, V.: Automating discovery of software tuning parameters. In: Workshop on Software Engineering for Adaptive and Self-managing Systems, SEAMS 2008 (2008)

    Google Scholar 

  7. Bruce, B.R., Petke, J., Harman, M.: Reducing energy consumption using genetic improvement. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, pp. 1327–1334. ACM, New York (2015)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  10. Do, H., Elbaum, S., Rothermel, G.: Supporting controlled experimentation with testing techniques: an infrastructure and its potential impact. Empirical Softw. Eng. 10(4), 405–435 (2005)

    Article  Google Scholar 

  11. Harman, M., Jia, Y., Langdon, W.B.: Strong higher order mutation-based test data generation. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE 2011, pp. 212–222 (2011)

    Google Scholar 

  12. Harman, M., Jia, Y., Reales Mateo, P., Polo, M.: Angels and monsters: an empirical investigation of potential test effectiveness and efficiency improvement from strongly subsuming higher order mutation. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, ASE 2014, pp. 397–408. ACM, New York (2014)

    Google Scholar 

  13. Harman, M., Jones, B.F.: Search-based software engineering. Inf. Softw. Technol. 43(14), 833–839 (2001)

    Article  Google Scholar 

  14. Harman, M., Langdon, W.B., Jia, Y., White, D.R., Arcuri, A., Clark, J.A.: The gismoe challenge: Constructing the pareto program surface using genetic programming to find better programs (keynote paper). In: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, ASE 2012, pp. 1–14. ACM, New York (2012)

    Google Scholar 

  15. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36(1), 267–306 (2009)

    MATH  Google Scholar 

  16. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Softw. Eng. 37(5), 649–678 (2011)

    Article  Google Scholar 

  17. Jia, Y., Harman, M.: MILU: a customizable, runtime-optimized higher order mutation testing tool for the full C language. In: Proceedings of the TAIC PART 2008, Windsor, UK, pp. 94–98, 29–31 August 2008

    Google Scholar 

  18. Jia, Y., Harman, M.: Higher order mutation testing. Inf. Softw. Technol. 51(10), 1379–1393 (2009). Source Code Analysis and Manipulation

    Article  Google Scholar 

  19. Jia, Y., Wu, F., Harman, M., Krinke, J.: Genetic improvement using higher order mutation. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion 2015, pp. 803–804. ACM, New York (2015)

    Google Scholar 

  20. Langdon, W., Harman, M.: Optimizing existing software with genetic programming. IEEE Trans. Evol. Comput. 19(1), 118–135 (2015)

    Article  Google Scholar 

  21. Langdon, W.B., Modat, M., Petke, J., Harman, M.: Improving 3D medical image registration CUDA software with genetic programming. In: Conference on Genetic and Evolutionary Computation, GECCO 2014 (2014)

    Google Scholar 

  22. Le Goues, C., Nguyen, T., Forrest, S., Weimer, W.: Genprog: a generic method for automatic software repair. IEEE Trans. Softw. Eng. 38(1), 54–72 (2012)

    Article  Google Scholar 

  23. Nanavati, J., Wu, F., Harman, M., Jia, Y., Krinke, J.: Mutation testing of memory-related operators. In: 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 1–10, April 2015

    Google Scholar 

  24. Neumann, G., Harman, M., Poulding, S.: Transformed Vargha-Delaney effect size. In: Barros, M., Labiche, Y. (eds.) SSBSE 2015. LNCS, vol. 9275, pp. 318–324. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  25. Papadakis, M., Jia, Y., Harman, M., Traon, Y.L.: Trivial compiler equivalence: a large scale empirical study of a simple, fast and effective equivalent mutant detection technique. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1, pp. 936–946, May 2015

    Google Scholar 

  26. Petke, J., Harman, M., Langdon, W.B., Weimer, W.: Using genetic improvement and code transplants to specialise a C++ program to a problem class. In: Nicolau, M., Krawiec, K., Heywood, M.I., Castelli, M., García-Sánchez, P., Merelo, J.J., Rivas Santos, V.M., Sim, K. (eds.) EuroGP 2014. LNCS, vol. 8599, pp. 137–149. Springer, Heidelberg (2014)

    Google Scholar 

  27. Petke, J., Langdon, W.B., Harman, M.: Applying genetic improvement to MiniSAT. In: Ruhe, G., Zhang, Y. (eds.) SSBSE 2013. LNCS, vol. 8084, pp. 257–262. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  28. White, D.R., Arcuri, A., Clark, J.A.: Evolutionary improvement of programs. IEEE Trans. Evol. Comput. 15(4), 515–538 (2011)

    Article  Google Scholar 

  29. Wu, F., Weimer, W., Harman, M., Jia, Y., Krinke, J.: Deep parameter optimisation. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, pp. 1375–1382. ACM, New York (2015)

    Google Scholar 

  30. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK

    Fan Wu, Mark Harman, Yue Jia & Jens Krinke

Authors
  1. Fan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Harman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jens Krinke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fan Wu .

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

Wu, F., Harman, M., Jia, Y., Krinke, J. (2016). HOMI: Searching Higher Order Mutants for Software Improvement. 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_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47106-8_2

  • 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