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International Symposium on Search Based Software Engineering

SSBSE 2017: Search Based Software Engineering pp 83–98Cite as

LIPS vs MOSA: A Replicated Empirical Study on Automated Test Case Generation

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 10452)

Abstract

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation.

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Notes

  1. 1.

    https://github.com/EvoSuite/evosuite/tree/master/client/src/main/java/org/evosuite/ga/metaheuristics/mosa.

  2. 2.

    https://github.com/EvoSuite/evosuite.

  3. 3.

    The number of branches reported here is sometimes slightly different from that of the original study because EvoSuite performs the instrumentation and counts the branches at the byte code, not source code, level.

  4. 4.

    http://www.joptimizer.com.

  5. 5.

    http://nd4j.org.

References

  1. Baker, R.D.: Modern permutation test software. In: Edgington, E. (ed.) Randomization Tests. Marcel Decker, New York (1995)

    Google Scholar 

  2. Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. Wiley, New York (1998)

    Google Scholar 

  3. Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. 4(1), 1–28 (2014)

    CrossRef  Google Scholar 

  4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2000)

    CrossRef  Google Scholar 

  5. Fraser, G., Arcuri, A.: Whole test suite generation. IEEE Trans. Softw. Eng. 39(2), 276–291 (2013)

    CrossRef  Google Scholar 

  6. Fraser, G., Arcuri, A.: A large-scale evaluation of automated unit test generation using EvoSuite. ACM Trans. Softw. Eng. Methodol. 24(2), 8:1–8:42 (2014). http://doi.acm.org/10.1145/2685612

    CrossRef  Google Scholar 

  7. Juzgado, N.J., Vegas, S.: The role of non-exact replications in software engineering experiments. Empir. Softw. Eng. 16(3), 295–324 (2011)

    CrossRef  Google Scholar 

  8. McMinn, P.: Search-based software test data generation: a survey. Softw. Test. Verif. Reliab. 14(2), 105–156 (2004)

    CrossRef  Google Scholar 

  9. Panichella, A., Kifetew, F., Tonella, P.: Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans. Softw. Eng. PP(99), 1 (2017). Pre-print available online

    CrossRef  Google Scholar 

  10. Panichella, A., Kifetew, F.M., Tonella, P.: Reformulating branch coverage as a many-objective optimization problem. In: 8th IEEE International Conference on Software Testing, Verification and Validation, ICST, pp. 1–10 (2015)

    Google Scholar 

  11. Scalabrino, S., Grano, G., Nucci, D., Oliveto, R., Lucia, A.: Search-based testing of procedural programs: iterative single-target or multi-target approach? In: Sarro, F., Deb, K. (eds.) SSBSE 2016. LNCS, vol. 9962, pp. 64–79. Springer, Cham (2016). doi:10.1007/978-3-319-47106-8_5

    Google Scholar 

  12. Shull, F., Basili, V.R., Carver, J., Maldonado, J.C., Travassos, G.H., Mendonça, M.G., Fabbri, S.: Replicating software engineering experiments: addressing the tacit knowledge problem. In: 2002 International Symposium on Empirical Software Engineering (ISESE 2002), 3–4 October 2002, Nara, pp. 7–16 (2002)

    Google Scholar 

  13. Shull, F., Carver, J.C., Vegas, S., Juzgado, N.J.: The role of replications in empirical software engineering. Empir. Softw. Eng. 13(2), 211–218 (2008)

    CrossRef  Google Scholar 

  14. Tonella, P.: Evolutionary testing of classes. In: ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2004), pp. 119–128. ACM (2004)

    Google Scholar 

  15. Vargha, A., Delaney, H.D.: A critique and improvement of the CL common language effect size statistics of Mcgraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Luxembourg, Luxembourg, Luxembourg

    Annibale Panichella

  2. Fondazione Bruno Kessler, Trento, Italy

    Fitsum Meshesha Kifetew & Paolo Tonella

Authors
  1. Annibale Panichella
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  2. Fitsum Meshesha Kifetew
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  3. Paolo Tonella
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Corresponding author

Correspondence to Annibale Panichella .

Editor information

Editors and Affiliations

  1. North Carolina State University, Morgantown, West Virginia, USA

    Tim Menzies

  2. Department of Computer Science, University College London, London, United Kingdom

    Dr. Justyna Petke

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Panichella, A., Kifetew, F.M., Tonella, P. (2017). LIPS vs MOSA: A Replicated Empirical Study on Automated Test Case Generation. In: Menzies, T., Petke, J. (eds) Search Based Software Engineering. SSBSE 2017. Lecture Notes in Computer Science(), vol 10452. Springer, Cham. https://doi.org/10.1007/978-3-319-66299-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-66299-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66298-5

  • Online ISBN: 978-3-319-66299-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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