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Software Improvement with Gin: A Case Study

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Search-Based Software Engineering (SSBSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11664))

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Abstract

We provide a case study for the usage of Gin, a genetic improvement toolbox for Java. In particular, we implemented a simple GP search and targeted two software optimisation properties: runtime and repair. We ran our search algorithm on Gson, a Java library for converting Java objects to JSON and vice-versa. We report on runtime improvements and fixes found. We provide all the new code and data on the dedicated website: https://github.com/justynapt/ssbseChallenge2019.

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Notes

  1. 1.

    https://github.com/google/gson.

  2. 2.

    https://cloc.org/.

  3. 3.

    Plugin used: https://github.com/STAMP-project/pitmp-maven-plugin.

  4. 4.

    Fixed test class is available on the submission’s website, in the input folder.

  5. 5.

    We decided to make this small change following insight that fixes usually require no more that four AST node edits [9].

  6. 6.

    https://docs.oracle.com/javase/7/docs/technotes/samples/hprof.html.

  7. 7.

    This mutant can be obtained by running with the text for the patch found in the output file of GPRuntime.

References

  1. An, G., Blot, A., Petke, J., Yoo, S.: PyGGI 2.0: language independent genetic improvement framework. In: Proceedings of the 12th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, Tallinn, Estonia, 26–30 August 2019. ACM (2019)

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  2. An, G., Kim, J., Yoo, S.: Comparing line and AST granularity level for program repair using PyGGI. In: Petke, J., Stolee, K.T., Langdon, W.B., Weimer, W. (eds.) Proceedings of the 4th International Genetic Improvement Workshop, GI@ICSE 2018, Gothenburg, Sweden, 2 June 2018, pp. 19–26. ACM (2018). https://doi.org/10.1145/3194810.3194814

  3. 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.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13–17 July 2019, pp. 985–993. ACM (2019). https://doi.org/10.1145/3321707.3321841

  4. Fraser, G., Arcuri, A.: Evosuite: automatic test suite generation for object-oriented software. In: Gyimóthy, T., Zeller, A. (eds.) SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11: 13th European Software Engineering Conference (ESEC-13), Szeged, Hungary, 5–9 September 2011, pp. 416–419. ACM (2011). https://doi.org/10.1145/2025113.2025179

  5. Haraldsson, S.O., Woodward, J.R., Brownlee, A.E.I., Cairns, D.: Exploring fitness and edit distance of mutated python programs. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 19–34. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55696-3_2

    Chapter  Google Scholar 

  6. Haraldsson, S.O., Woodward, J.R., Brownlee, A.E.I., Siggeirsdottir, K.: Fixing bugs in your sleep: how genetic improvement became an overnight success. In: Bosman, P.A.N. (ed.) Genetic and Evolutionary Computation Conference, Berlin, Germany, 15–19 July 2017, Companion Material Proceedings, pp. 1513–1520. ACM (2017). https://doi.org/10.1145/3067695.3082517

  7. Langdon, W.B., Lam, B.Y.H., Petke, J., Harman, M.: Improving CUDA DNA analysis software with genetic programming. In: Silva, S., Esparcia-Alcázar, A.I. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11–15 July 2015, pp. 1063–1070. ACM (2015). https://doi.org/10.1145/2739480.2754652

  8. 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). https://doi.org/10.1109/TSE.2011.104

  9. Martinez, M., Monperrus, M.: Mining software repair models for reasoning on the search space of automated program fixing. Empirical Softw. Eng. 20(1), 176–205 (2015). https://doi.org/10.1007/s10664-013-9282-8

  10. Petke, J., Haraldsson, S.O., Harman, M., Langdon, W.B., White, D.R., Woodward, J.R.: Genetic improvement of software: a comprehensive survey. IEEE Trans. Evol. Comput. 22(3), 415–432 (2018). https://doi.org/10.1109/TEVC.2017.2693219

  11. White, D.R.: GI in no time. In: Bosman, P.A.N. (ed.) Genetic and Evolutionary Computation Conference, Berlin, Germany, 15–19 July 2017, Companion Material Proceedings, pp. 1549–1550. ACM (2017). https://doi.org/10.1145/3067695.3082515

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Acknowledgement

The work was funded by the UK EPSRC grant EP/P023991/1 and Carnegie Trust grant RIG008300.

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Authors and Affiliations

  1. University College London, London, UK

    Justyna Petke

  2. University of Stirling, Stirling, UK

    Alexander E. I. Brownlee

Authors
  1. Justyna Petke
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  2. Alexander E. I. Brownlee
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Corresponding author

Correspondence to Justyna Petke .

Editor information

Editors and Affiliations

  1. SnT/University of Luxembourg, Luxembourg, Luxembourg

    Shiva Nejati

  2. University of South Carolina, Columbia, SC, USA

    Gregory Gay

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Petke, J., Brownlee, A.E.I. (2019). Software Improvement with Gin: A Case Study. In: Nejati, S., Gay, G. (eds) Search-Based Software Engineering. SSBSE 2019. Lecture Notes in Computer Science(), vol 11664. Springer, Cham. https://doi.org/10.1007/978-3-030-27455-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-27455-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27454-2

  • Online ISBN: 978-3-030-27455-9

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