Skip to main content
SpringerLink
Log in

Particle Swarm Based Evolution and Generation of Test Data Using Mutation Testing

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9790))

Included in the following conference series:

Abstract

Adequate test data generation is a vital task involved in the process software testing. Process of mutation testing, a fault-based testing technique, generates mutants of the program under test (PUT) by applying mutation operators. These mutants can assist in finding test cases that have the potential to detect faults in the PUT. Particle Swarm Optimisation (PSO) share similar working characteristics with Genetic Algorithm (GA) which has already been applied to test data generation using mutation testing. In this paper, applicability of PSO for the generation of test data with mutation testing is explored. The results obtained by empirical evaluation of the proposed approach on benchmark C programs are presented. The evaluated results show that the test cases generated from the technique proposed kills substantial number of mutants and therefore, has a scope of exploring its performance in the area of search based test case generation.

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

References

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

    Article  Google Scholar 

  2. Hamlet, R.G.: Testing programs with the aid of a compiler. IEEE Trans. Softw. Eng. 3(4), 279–290 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  3. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Softw. Eng. 37(5), 649–678 (2010)

    Article  Google Scholar 

  4. Souza, F.C., Papadakis, M., Durelli, V.H., Delamaro, M.E.: Test data generation techniques for mutation testing: a systematic mapping. In: Proceedings of the 11th IESELAW, pp. 1–14 (2014)

    Google Scholar 

  5. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  7. Jordehi, A.R., Jasni, J.: Particle swarm optimisation for discrete optimisation problems: a review. Artif. Intell. Rev. 43(2), 243–258 (2015)

    Article  Google Scholar 

  8. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  9. Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (2004)

    Google Scholar 

  10. Jones, B.F., Eyres, D.E., Sthamer, H.H.: Strategy for using genetic algorithms to automate branch and fault-based testing. Comput. J. 41, 98–107 (1998)

    Article  Google Scholar 

  11. Bottaci, L.: A genetic algorithm fitness function for mutation testing. In: 8th Wrokshop on Software Engineering Using Metaheuristic Innovative Algorithm (SEMINAL 2001), pp. 3–7 (2001)

    Google Scholar 

  12. Baudry, B., Hanh, V.L., Jzquel, J.-M., Traon, Y.L.: Trustable components: yet another mutation-based approach. In: Wong, W.E. (ed.) Mutation Testing for the New Century, vol. 24, pp. 47–54. Springer, New York (2001)

    Chapter  Google Scholar 

  13. May, P., Timmis, J., Mander, K.: Immune and evolutionary approaches to software mutation testing. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 336–347. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Masud, M.M., Nayak, A., Zaman, M., Bansal, N.: A strategy for mutation testing using genetic algorithms. In: Canadian Conference on Electrical and Computer Engineering, pp. 1049–1052. IEEE (2005)

    Google Scholar 

  15. Molinero, C., Núñez, M., Andrés, C.: Combining genetic algorithms and mutation testing to generate test sequences. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009, Part I. LNCS, vol. 5517, pp. 343–350. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  16. Mishra, K.K., Tiwari, S., Kumar, A., Misra, A.K.: An approach for mutation testing using elitist genetic algorithm. In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 5, pp. 426–429. IEEE (2010)

    Google Scholar 

  17. Moncao, A.C., Camilo-Junior, C.G., Queiroz, L.T., Rodrigues, C.L., Leitao-Junior, P.d., Vincenzi, A.: Shrinking a database to perform SQL mutation tests using an evolutionary algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2533–2539. IEEE (2013)

    Google Scholar 

  18. Fraser, G., Zeller, A.: Mutation-driven generation of unit tests and oracles. IEEE Trans. Softw. Eng. 38(2), 278–292 (2012)

    Article  Google Scholar 

  19. Haga, H., Suehiro, A.: Automatic test case generation based on genetic algorithm and mutation analysis. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 119–123. IEEE (2012)

    Google Scholar 

  20. Bashir, M.B., Nadeem, A.: A fitness function for evolutionary mutation testing of object-oriented programs. In: IEEE 9th International Conference on Emerging Technologies (ICET), pp. 1–6. IEEE (2013)

    Google Scholar 

  21. Rad, M.F., Bahrekazemi, S.: Applying genetic evolutionary, bacteriological and quantum evolutionary algorithm for improving performance optimization segment of test data sets in mutation testing method. Int. J. Soft Comput. Softw. Eng. (JSCSE) 167–186 (2014)

    Google Scholar 

  22. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  23. Andalib Sahnehsaraei, M., Mahmoodabadi, M.J., Taherkhorsandi, M., Castillo-Villar, K.K., Mortazavi Yazdi, S.M.: A hybrid global optimization algorithm: particle swarm optimization in association with a genetic algorithm. In: Zhu, Q., Azar, A.T. (eds.) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol. 319, pp. 45–86. Springer, Switzerland (2014)

    Google Scholar 

  24. Li, Z., Liu, X., Duan, X.: Comparative research on particle swarm optimization and genetic. Comput. Inf. Sci. 3(1), 120–127 (2010)

    Google Scholar 

  25. Baudry, B., Fleurey, F., Jzquel, J.-M., Traon, Y.L.: From genetic to bacteriological algorithms for mutation-based testing. In: International Symposium on Software Reliability, pp. 73–96. Wiley (2005)

    Google Scholar 

  26. Clerc, M., Kennedy, J.: The particle swarm explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  27. Shi, Y.: Feature article on particle swarm optimization. In: IEEE Neural Network Society, p. 813 (2004)

    Google Scholar 

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

    Article  Google Scholar 

  29. Hutchins, M., Foster, H., Goradia, T., Ostrand, T.: Experiments on the effectiveness of dataflow- and control flow-based test adequacy criteria. In: Proceedings of the 16th International Conference on Software Engineering, pp. 191–200 (1994)

    Google Scholar 

  30. Jia, Y., Harman, M.: MILU: a customizable, runtime-optimized higher order mutation testing tool for the full C language. In: Testing: Academic and Industrial Conference Practice and Research Techniques (TAIC PART 2008), Windsor, pp. 94–98. IEEE (2008)

    Google Scholar 

  31. Agarwal, H., Demillo, R., Hathaway, R., Hsu, W., Krauser, E., Martin, R.: Design of mutant operators for the C programming language. Technical report, March 1989

    Google Scholar 

  32. Rad, M.F., Akbari, F., Bakht, A.J.: Implementation of common genetic and bacteriological algorithms in optimizing testing data in mutation testing. In: International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, MSIT, New Delhi, India

    Nishtha Jatana & Prateek Kumar

  2. USICT, GGS Indraprastha University, New Delhi, India

    Bharti Suri

  3. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Department of Computer Engineering, Delhi Technological University, New Delhi, India

    Amit Roy Choudhury

Authors
  1. Nishtha Jatana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bharti Suri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjay Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prateek Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amit Roy Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay Misra .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Jatana, N., Suri, B., Misra, S., Kumar, P., Choudhury, A.R. (2016). Particle Swarm Based Evolution and Generation of Test Data Using Mutation Testing. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42092-9_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42091-2

  • Online ISBN: 978-3-319-42092-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation