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Generating Efficient Mutation Operators for Search-Based Model-Driven Engineering

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Theory and Practice of Model Transformation (ICMT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10374))

Abstract

Software engineers are frequently faced with tasks that can be expressed as optimization problems. To support them with automation, search-based model-driven engineering combines the abstraction power of models with the versatility of meta-heuristic search algorithms. While current approaches in this area use genetic algorithms with fixed mutation operators to explore the solution space, the efficiency of these operators may heavily depend on the problem at hand. In this work, we propose FitnessStudio, a technique for generating efficient problem-tailored mutation operators automatically based on a two-tier framework. The lower tier is a regular meta-heuristic search whose mutation operator is “trained” by an upper-tier search using a higher-order model transformation. We implemented this framework using the Henshin transformation language and evaluated it in a benchmark case, where the generated mutation operators enabled an improvement to the state of the art in terms of result quality, without sacrificing performance.

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Notes

  1. 1.

    Details are found at https://wiki.eclipse.org/Henshin_Transformation_Meta-Model.

  2. 2.

    For the original spreadsheet, see http://tinyurl.com/z75n7fc – for the computation of medians, see our spreadsheet at https://git.io/vyGpJ.

References

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

    Article  Google Scholar 

  2. Fleck, M., Troya, J., Wimmer, M.: Search-based model transformations. Softw. Evol. Process 28, 1081–1117 (2016)

    Article  Google Scholar 

  3. Debreceni, C., Ráth, I., Varró, D., Carlos, X., Mendialdua, X., Trujillo, S.: Automated model merge by design space exploration. In: Stevens, P., Wąsowski, A. (eds.) FASE 2016. LNCS, vol. 9633, pp. 104–121. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49665-7_7

    Chapter  Google Scholar 

  4. Fleck, M., Troya, J., Kessentini, M., Wimmer, M., Alkhazi, B.: Model transformation modularization as a many-objective optimization problem. IEEE Trans. Softw. Eng. (2017)

    Google Scholar 

  5. Abdeen, H., Varró, D., Sahraoui, H., Nagy, A.S., Debreceni, C., Hegedüs, Á., Horváth, Á.: Multi-objective optimization in rule-based design space exploration. In: ASE, pp. 289–300. ACM (2014)

    Google Scholar 

  6. Fleck, M., Troya, J., Wimmer, M.: Search-based model transformations with MOMoT. In: Van Gorp, P., Engels, G. (eds.) ICMT 2016. LNCS, vol. 9765, pp. 79–87. Springer, Cham (2016). doi:10.1007/978-3-319-42064-6_6

    Chapter  Google Scholar 

  7. Zschaler, S., Mandow, L.: Towards model-based optimisation: using domain knowledge explicitly. In: Milazzo, P., Varró, D., Wimmer, M. (eds.) STAF 2016. LNCS, vol. 9946, pp. 317–329. Springer, Cham (2016). doi:10.1007/978-3-319-50230-4_24

    Chapter  Google Scholar 

  8. Fleck, M., Troya, J., Wimmer, M.: The class responsibility assignment case. In: TTC, pp. 1–8 (2016)

    Google Scholar 

  9. Arendt, T., Biermann, E., Jurack, S., Krause, C., Taentzer, G.: Henshin: advanced concepts and tools for in-place EMF model transformations. In: Petriu, D.C., Rouquette, N., Haugen, Ø. (eds.) MODELS 2010. LNCS, vol. 6394, pp. 121–135. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16145-2_9

    Chapter  Google Scholar 

  10. Woodward, J.R., Swan, J.: The automatic generation of mutation operators for genetic algorithms. In: GECCO, pp. 67–74 (2012)

    Google Scholar 

  11. Mehlhorn, K.: Graph Algorithms and NP-Completeness. Springer, New York (1984)

    MATH  Google Scholar 

  12. Tichy, M., Krause, C., Liebel, G.: Detecting performance bad smells for Henshin model transformations. In: AMT (2013)

    Google Scholar 

  13. Born, K., Schulz, S., Strüber, D., John, S.: Solving the class responsibility assignment case with Henshin and a genetic algorithm. In: TTC, pp. 45–54 (2016)

    Google Scholar 

  14. Nagy, A.S., Szárnyas, G.: Class responsiblity assignment case: a Viatra-DSE solution. In: TTC, pp. 39–44 (2016)

    Google Scholar 

  15. Strüber, D., Schulz, S.: A tool environment for managing families of model transformation rules. In: Echahed, R., Minas, M. (eds.) ICGT 2016. LNCS, vol. 9761, pp. 89–101. Springer, Cham (2016). doi:10.1007/978-3-319-40530-8_6

    Chapter  Google Scholar 

  16. Strüber, D.: Model-driven engineering in the large: refactoring techniques for models and model transformation systems. Ph.D. thesis, Philipps-Universität Marburg (2016)

    Google Scholar 

  17. Mkaouer, M.W., Kessentini, M.: Model transformation using multiobjective optimization. Adv. Comput. 92, 161–202 (2014)

    Article  Google Scholar 

  18. Kehrer, T., Taentzer, G., Rindt, M., Kelter, U.: Automatically deriving the specification of model editing operations from meta-models. In: Van Gorp, P., Engels, G. (eds.) ICMT 2016. LNCS, vol. 9765, pp. 173–188. Springer, Cham (2016). doi:10.1007/978-3-319-42064-6_12

    Chapter  Google Scholar 

  19. Popoola, S., Kolovos, D.S., Rodriguez, H.H.: EMG: a domain-specific transformation language for synthetic model generation. In: Van Gorp, P., Engels, G. (eds.) ICMT 2016. LNCS, vol. 9765, pp. 36–51. Springer, Cham (2016). doi:10.1007/978-3-319-42064-6_3

    Chapter  Google Scholar 

  20. Troya, J., Bergmayr, A., Burgueño, L., Wimmer, M.: Towards systematic mutations for and with ATL model transformations. In: Workshop on Mutation Analysis, pp. 1–10 (2015)

    Google Scholar 

  21. Alhwikem, F., Paige, R.F., Rose, L., Alexander, R.: A systematic approach for designing mutation operators for MDE languages. In: MoDEVVa, pp. 54–59 (2016)

    Google Scholar 

  22. Gómez-Abajo, P., Guerra, E., de Lara, J.: A domain-specific language for model mutation and its application to the automated generation of exercises. In: Computer Languages, Systems & Structures (2016)

    Google Scholar 

  23. Smullen, D., Gillett, J., Heron, J., Rahnamayan, S.: Genetic algorithm with self-adaptive mutation controlled by chromosome similarity. In: CEC, pp. 504–511. IEEE (2014)

    Google Scholar 

  24. Martin, M.A., Tauritz, D.R.: Evolving black-box search algorithms employing genetic programming. In: GECCO, companion volume, pp. 1497–1504. ACM (2013)

    Google Scholar 

  25. Strüber, D., Rubin, J., Arendt, T., Chechik, M., Taentzer, G., Plöger, J.: RuleMerger: automatic construction of variability-based model transformation rules. In: Stevens, P., Wąsowski, A. (eds.) FASE 2016. LNCS, vol. 9633, pp. 122–140. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49665-7_8

    Chapter  Google Scholar 

  26. Strüber, D., Plöger, J., Acreţoaie, V.: Clone detection for graph-based model transformation languages. In: Van Gorp, P., Engels, G. (eds.) ICMT 2016. LNCS, vol. 9765, pp. 191–206. Springer, Cham (2016). doi:10.1007/978-3-319-42064-6_13

    Chapter  Google Scholar 

  27. Ahmadian, A.S., Strüber, D., Riediger, V., Jürjens, J.: Model-based privacy analysis in industrial ecosystems. In: ECMFA. Springer (2017)

    Google Scholar 

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Acknowledgement

This research was partially supported by the research project Visual Privacy Management in User Centric Open Environments (supported by the EU’s Horizon 2020 programme, Proposal number: 653642).

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Authors and Affiliations

  1. University of Koblenz and Landau, Koblenz, Germany

    Daniel Strüber

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  1. Daniel Strüber
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Correspondence to Daniel Strüber .

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Editors and Affiliations

  1. Department of Computer Science, Universidad Autónoma de Madrid, Madrid, Spain

    Esther Guerra

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Mark van den Brand

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Strüber, D. (2017). Generating Efficient Mutation Operators for Search-Based Model-Driven Engineering. In: Guerra, E., van den Brand, M. (eds) Theory and Practice of Model Transformation. ICMT 2017. Lecture Notes in Computer Science(), vol 10374. Springer, Cham. https://doi.org/10.1007/978-3-319-61473-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-61473-1_9

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