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Injecting Social Diversity in Multi-objective Genetic Programming: The Case of Model Well-Formedness Rule Learning

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Search-Based Software Engineering (SSBSE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11036))

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Abstract

Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artifacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behavior of an artifact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, remains when adapted to MDE and hinders the results of the learning. We aim at showing in this paper that an improvement in populations’ social diversity carried out during the evolutionary computation will lead to more efficient search, faster convergence, and more generalizable results. We ascertain improvements are due to our changes on the search strategy with an empirical evaluation featuring the case of learning well-formedness rules in MDE with a multi-objective genetic algorithm. The obtained results are striking, and show that semantic diversity allows a rapid convergence toward the near-optimal solutions. Moreover, when the semantic diversity is used as for crowding distance, this convergence is uniform through a hundred of runs.

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Notes

  1. 1.

    http://www.omg.org/spec/OCL/.

  2. 2.

    All experiment data is available at http://www-ens.iro.umontreal.ca/~batotedo/ssdm_exp/.

References

  1. Schmidt, D.C.: Model-driven engineering. IEEE Comput. Soc. 39(2), 25 (2006)

    Article  MathSciNet  Google Scholar 

  2. Selic, B.: What will it take? A view on adoption of model-based methods in practice. Int. J. Softw. Syst. Model. 11(4), 513–526 (2012)

    Article  Google Scholar 

  3. Whittle, J., Hutchinson, J., Rouncefield, M.: The state of practice in model-driven engineering. IEEE Softw. 31, 79–85 (2014)

    Article  Google Scholar 

  4. Kessentini, M., Kessentini, W., Sahraoui, H., Boukadoum, M., Ouni, A.: Design defects detection and correction by example. In: Proceedings of the International Conference on Program Comprehension, pp. 81–90 (2011)

    Google Scholar 

  5. Saada, H., Dolques, X., Huchard, M., Nebut, C., Sahraoui, H.: Generation of operational transformation rules from examples of model transformations. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 546–561. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33666-9_35

    Chapter  Google Scholar 

  6. Baki, I., Sahraoui, H.: Multi-step learning and adaptive search for learning complex model transformations from examples. ACM Trans. Softw. Eng. Methodol. X, 36 (2015)

    Google Scholar 

  7. Faunes, M., Cadavid, J., Baudry, B., Sahraoui, H., Combemale, B.: Automatically searching for metamodel well-formedness rules in examples and counter-examples. In: Moreira, A., Schätz, B., Gray, J., Vallecillo, A., Clarke, P. (eds.) MODELS 2013. LNCS, vol. 8107, pp. 187–202. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41533-3_12

    Chapter  Google Scholar 

  8. Batot, E., Sahraoui, H.: A generic framework for model-set selection for the unification of testing and learning MDE tasks. In: Proceedings of the International Conference on Model-Driven Engineering Languages and Systems. ACM (2016)

    Google Scholar 

  9. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum Associates Inc., Hillsdale (1985)

    Google Scholar 

  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  11. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  12. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  13. de Jong, E.D., Watson, R.A., Pollack, J.B.: Reducing bloat and promoting diversity using multi-objective methods. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001, pp. 11–18 (2001)

    Google Scholar 

  14. Bersano-Begey, T.F.: Controlling exploration, diversity and escaping local optima in GP: adapting weights of training sets to model resource consumption. In: Koza, J.R. (ed.) Late Breaking Papers at the 1997 Genetic Programming Conference, pp. 7–10 (1997)

    Google Scholar 

  15. Soule, T., Foster, J.A.: Effects of code growth and parsimony pressure on populations in genetic programming. Evol. Comput. 6(4), 293–309 (1998)

    Article  Google Scholar 

  16. Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evol. Comput. 14(3), 309–344 (2006)

    Article  Google Scholar 

  17. Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Programm. Evol. Mach. 15(2), 195–214 (2014)

    Article  Google Scholar 

  18. 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 

  19. Ekárt, A., Németh, S.Z.: A metric for genetic programs and fitness sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 259–270. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-540-46239-2_19

    Chapter  Google Scholar 

  20. Wyns, B., De Bruyne, P., Boullart, L.: Characterizing diversity in genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 250–259. Springer, Heidelberg (2006). https://doi.org/10.1007/11729976_22

    Chapter  Google Scholar 

  21. Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)

    Article  Google Scholar 

  22. McPhee, N.F., Hopper, N.J.: Analysis of genetic diversity through population history. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1112–1120. Morgan Kaufmann Publishers Inc. (1999)

    Google Scholar 

  23. McPhee, N.F., Ohs, B., Hutchison, T.: Semantic building blocks in genetic programming. In: O’Neill, M., et al. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 134–145. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78671-9_12

    Chapter  Google Scholar 

  24. Baudry, B., Monperrus, M.: The multiple facets of software diversity: recent developments in year 2000 and beyond. ACM Comput. Surv. 48(1), 16:1–16:26 (2015)

    Article  Google Scholar 

  25. Giraldo, F.D., EspaÃśa, S., Pastor, O.: Analysing the concept of quality in model-driven engineering literature: a systematic review. In: 2014 IEEE Eighth International Conference on Research Challenges in Information Science, RCIS, pp. 1–12, May 2014

    Google Scholar 

  26. Wu, H.: Generating metamodel instances satisfying coverage criteria via SMT solving. In: Proceedings of the International Conference on Model-Driven Engineering and Software Development, pp. 40–51 (2016)

    Google Scholar 

  27. Ferdjoukh, A., Galinier, F., Bourreau, E., Chateau, A., Nebut, C.: Measuring differences to compare sets of models and improve diversity in MDE. In: International Conference on Software Engineering Advances, ICSEA, Athens, Greece, October 2017

    Google Scholar 

  28. Sánchez-Cuadrado, J., de Lara, J., Guerra, E.: Bottom-up meta-modelling: an interactive approach. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 3–19. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33666-9_2

    Chapter  Google Scholar 

  29. López-Fernández, J.J., Guerra, E., de Lara, J.: Example-based validation of domain-specific visual languages. In: Proceedings of the International Conference on Software Language Engineering, SLE 2015, pp. 101–112 (2015)

    Google Scholar 

  30. Ryan, C.: Racial harmony in genetic algorithms (1994)

    Google Scholar 

  31. Dabhi, V.K., Chaudhary, S.: A survey on techniques of improving generalization ability of genetic programming solutions. CoRR abs/1211.1119 (2012)

    Google Scholar 

  32. Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. In: Willett, P. (ed.) Document Retrieval Systems, pp. 132–142 (1988)

    Google Scholar 

  33. Harman, M., Jia, Y., Zhang, Y.: Achievements, open problems and challenges for search based software testing. In: Proceedings of the International Conference on Software Testing Verification and Validation, pp. 1–12 (2015)

    Google Scholar 

  34. Cadavid, J.J., Combemale, B., Baudry, B.: Ten years of meta-object facility: an analysis of metamodeling practices. AtlanMod, Research Report RR-7882 (2012)

    Google Scholar 

  35. Fortin, F.-A., Parizeau, M.: Revisiting the NSGA-II crowding-distance computation. In: Proceedings of International Conference on Genetic and Evolutionary Computation, GECCO. ACM (2013)

    Google Scholar 

  36. Gogolla, M., Vallecillo, A., Burgueno, L., Hilken, F.: Employing classifying terms for testing model transformations. In: Proceedings of the International Conference on Model-Driven Engineering Languages and Systems, pp. 312–321 (2015)

    Google Scholar 

  37. Hassam, K., Sadou, S., Fleurquin, R.: Adapting OCL constraints after a refactoring of their model using an MDE process. In: 9th Edition of the BElgian-NEtherlands Software eVOLution Seminar, pp. 16–27 (2010)

    Google Scholar 

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Authors and Affiliations

  1. GEODES, DIRO, Université de Montréal, Montreal, Canada

    Edouard Batot & Houari Sahraoui

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  1. Edouard Batot
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  2. Houari Sahraoui
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Correspondence to Edouard Batot or Houari Sahraoui .

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Editors and Affiliations

  1. State University of Maringá, Maringá, Brazil

    Thelma Elita Colanzi

  2. University of Sheffield, Sheffield, United Kingdom

    Phil McMinn

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Batot, E., Sahraoui, H. (2018). Injecting Social Diversity in Multi-objective Genetic Programming: The Case of Model Well-Formedness Rule Learning. In: Colanzi, T., McMinn, P. (eds) Search-Based Software Engineering. SSBSE 2018. Lecture Notes in Computer Science(), vol 11036. Springer, Cham. https://doi.org/10.1007/978-3-319-99241-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-99241-9_8

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