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Models, More Models, and Then a Lot More

  • Önder BaburEmail author
  • Loek Cleophas
  • Mark van den Brand
  • Bedir Tekinerdogan
  • Mehmet Aksit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10748)

Abstract

With increased adoption of Model-Driven Engineering, the number of related artefacts in use, such as models, metamodels and transformations, greatly increases. To confirm this, we present quantitative evidence from both academia — in terms of repositories and datasets — and industry — in terms of large domain-specific language ecosystems. To be able to tackle this dimension of scalability in MDE, we propose to treat the artefacts as data, and apply various techniques — ranging from information retrieval to machine learning — to analyse and manage those artefacts in a holistic, scalable and efficient way.

Keywords

Model-Driven Engineering Scalability Model analytics Data mining Machine learning 

Notes

Acknowledgments

This work is supported by the 4TU.NIRICT Research Community Funding on Model Management and Analytics in the Netherlands.

References

  1. 1.
    Babur, Ö.: Statistical analysis of large sets of models. In: 31th IEEE/ACM International Conference on Automated Software Engineering (ASE 2016), Singapore, 3–7 September 2016 (2016)Google Scholar
  2. 2.
    Babur, Ö., Cleophas, L., van den Brand, M.: Hierarchical clustering of metamodels for comparative analysis and visualization. In: 2016 Proceedings of the 12th European Conference on Modelling Foundations and Applications, pp. 2–18 (2016)Google Scholar
  3. 3.
    Baki, I., Sahraoui, H.A.: Multi-step learning and adaptive search for learning complex model transformations from examples. ACM Trans. Softw. Eng. Methodol. 25(3), 20:1–20:37 (2016).  https://doi.org/10.1145/2904904 CrossRefGoogle Scholar
  4. 4.
    Basciani, F., Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Automated clustering of metamodel repositories. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 342–358. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39696-5_21 Google Scholar
  5. 5.
    Basciani, F., Di Rocco, J., Di Ruscio, D., Di Salle, A., Iovino, L., Pierantonio, A.: MDEForge: an extensible web-based modeling platform. In: Proceedings of the 2nd International Workshop on Model-Driven Engineering on and for the Cloud Co-located with the 17th International Conference on Model Driven Engineering Languages and Systems, CloudMDE@MoDELS 2014, Valencia, Spain, 30 September 2014, pp. 66–75 (2014). http://ceur-ws.org/Vol-1242/paper10.pdf
  6. 6.
    Benelallam, A., Gómez, A., Tisi, M., Cabot, J.: Distributed model-to-model transformation with ATL on MapReduce. In: Proceedings of the 2015 ACM SIGPLAN International Conference on Software Language Engineering, pp. 37–48. ACM (2015)Google Scholar
  7. 7.
    Bislimovska, B., Bozzon, A., Brambilla, M., Fraternali, P.: Textual and content-based search in repositories of web application models. TWEB 8(2), 11:1–11:47 (2014).  https://doi.org/10.1145/2579991 CrossRefGoogle Scholar
  8. 8.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice, 1st edn. Morgan & Claypool Publishers, San Rafael (2012)Google Scholar
  9. 9.
    Burgueño, L., Wimmer, M., Vallecillo, A.: Towards distributed model transformations with LinTra (2016)Google Scholar
  10. 10.
    Catal, C., Diri, B.: A systematic review of software fault prediction studies. Expert Syst. Appl. 36(4), 7346–7354 (2009)CrossRefGoogle Scholar
  11. 11.
    Deissenboeck, F., Hummel, B., Juergens, E., Pfaehler, M., Schaetz, B.: Model clone detection in practice. In: Proceedings of the 4th International Workshop on Software Clones, pp. 57–64. ACM (2010)Google Scholar
  12. 12.
    Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Mining metrics for understanding metamodel characteristics. In: Proceedings of the 6th International Workshop on Modeling in Software Engineering, MiSE 2014, pp. 55–60. ACM, New York (2014). http://doi.acm.org/10.1145/2593770.2593774
  13. 13.
    Hartmann, T., Moawad, A., Fouquet, F., Nain, G., Klein, J., Traon, Y.L., Jezequel, J.M.: Model-driven analytics: connecting data, domain knowledge, and learning. arXiv preprint arXiv:1704.01320 (2017)
  14. 14.
    Hebig, R., Ho-Quang, T., Chaudron, M.R.V., Robles, G., Fernández, M.A.: The quest for open source projects that use UML: mining GitHub. In: Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, Saint-Malo, France, 2–7 October 2016, pp. 173–183 (2016). http://dl.acm.org/citation.cfm?id=2976778
  15. 15.
    Hotho, A., Nürnberger, A., Paass, G.: A brief survey of text mining. LDV Forum 20(1), 19–62 (2005). http://www.jlcl.org/2005_Heft1/19-62_HothoNuernbergerPaass.pdf Google Scholar
  16. 16.
    Kagdi, H., Collard, M.L., Maletic, J.I.: A survey and taxonomy of approaches for mining software repositories in the context of software evolution. J. Softw. Maint. Evol. 19(2), 77–131 (2007).  https://doi.org/10.1002/smr.344 CrossRefGoogle Scholar
  17. 17.
    Kolovos, D.S., Matragkas, N.D., Korkontzelos, I., Ananiadou, S., Paige, R.F.: Assessing the use of eclipse MDE technologies in open-source software projects. In: OSS4MDE@ MoDELS, pp. 20–29 (2015)Google Scholar
  18. 18.
    Kolovos, D.S., Rose, L.M., Matragkas, N., Paige, R.F., Guerra, E., Cuadrado, J.S., De Lara, J., Ráth, I., Varró, D., Tisi, M., Cabot, J.: A research roadmap towards achieving scalability in model driven engineering. In: Proceedings of the Workshop on Scalability in Model Driven Engineering, BigMDE 2013, pp. 2:1–2:10. ACM, New York (2013). http://doi.acm.org/10.1145/2487766.2487768
  19. 19.
    Mengerink, J.G., Serebrenik, A., Schiffelers, R.R., van den Brand, M.G.: Automated analyses of model-driven artifacts. IWSM Mensura (2017, to appear)Google Scholar
  20. 20.
    Omran, F.N.A.A., Treude, C.: Choosing an NLP library for analyzing software documentation: a systematic literature review and a series of experiments. In: Proceedings of the 14th International Conference on Mining Software Repositories, MSR 2017, Buenos Aires, Argentina, 20–28 May 2017, pp. 187–197 (2017). https://doi.org/10.1109/MSR.2017.42
  21. 21.
    Roy, C.K., Cordy, J.R., Koschke, R.: Comparison and evaluation of code clone detection techniques and tools: a qualitative approach. Sci. Comput. Program. 74(7), 470–495 (2009). http://www.sciencedirect.com/science/article/pii/S0167642309000367 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Störrle, H.: Towards clone detection in UML domain models. Softw. Syst. Model. 12(2), 307–329 (2013)CrossRefGoogle Scholar
  23. 23.
    Störrle, H., Hebig, R., Knapp, A.: An index for software engineering models. In: Joint Proceedings of MODELS 2014 Poster Session and the ACM Student Research Competition (SRC) Co-located with the 17th International Conference on Model Driven Engineering Languages and Systems (MODELS 2014), Valencia, Spain, 28 September–3 October 2014, pp. 36–40 (2014). http://ceur-ws.org/Vol-1258/poster8.pdf
  24. 24.
    Whittle, J., Hutchinson, J., Rouncefield, M.: The state of practice in model-driven engineering. IEEE Softw. 31(3), 79–85 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Önder Babur
    • 1
    Email author
  • Loek Cleophas
    • 1
  • Mark van den Brand
    • 1
  • Bedir Tekinerdogan
    • 2
  • Mehmet Aksit
    • 3
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Wageningen University & ResearchWageningenThe Netherlands
  3. 3.University of TwenteEnschedeThe Netherlands

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