Advertisement

Automated Clustering of Metamodel Repositories

  • Francesco Basciani
  • Juri Di Rocco
  • Davide Di Ruscio
  • Ludovico Iovino
  • Alfonso Pierantonio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Over the last years, several model repositories have been proposed in response to the need of the MDE community for advanced systems supporting the reuse of modeling artifacts. Modelers can interact with MDE repositories with different intents ranging from merely repository browsing, to searching specific artifacts satisfying precise requirements. The organization and browsing facilities provided by current repositories is limited since they do not produce structured overviews of the contained artifacts, and the ategorization mechanisms (if any) are based on manual activities. When dealing with large numbers of modeling artifacts, such limitations increase the effort for managing and reusing artifacts stored in model repositories. By focusing on metamodel repositories, in this paper we propose the application of clustering techniques to automatically organize stored metamodels and to provide users with overviews of the application domains covered by the available metamodels. The approach has been implemented in the MDEForge repository.

Keywords

Model Driven Engineering Model repositories Metamodel clustering MDEForge 

References

  1. 1.
    Schmidt, D.C.: Guest editor’s introduction: model-driven engineering. Computer 39, 25–31 (2006)CrossRefGoogle Scholar
  2. 2.
    France, R.B., Bieman, J.M., Mandalaparty, S.P., Cheng, B.H.C., Jensen, A.: Repository for Model Driven Development (ReMoDD). In: Proceedings of 34th International Conference on Software Engineering (ICSE), pp. 1471–1472. IEEE (2012)Google Scholar
  3. 3.
    Hein, C., Ritter, T., Wagner, M.: Model-driven tool integration with modelbus. In: Workshop Future Trends of Model-Driven Development at International Conference on Enterprise Information Systems (ICEIS), pp. 50–52 (2009)Google Scholar
  4. 4.
    Karasneh, B., Chaudron, M.R.V.: Online Img2UML repository: an online repository for UML models. In: Proceedings of the 3rd International Workshop on Experiences and Empirical Studies in Software Modeling at MoDELS, pp. 61–66 (2013)Google Scholar
  5. 5.
    Koegel, M., Helming, J.: EMFStore: a model repository for EMF models. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010, pp. 307–308. ACM (2010)Google Scholar
  6. 6.
    Kutsche, R., Milanovic, N., Bauhoff, G., Baum, T., Cartsburg, M., Kumpe, D., Widiker, J.: BIZYCLE: model-based interoperability platform for software and data integration. In: Proceedings of MDTPI at ECMDA (2008)Google Scholar
  7. 7.
    Bislimovska, B., Bozzon, A., Brambilla, M., Fraternali, P.: Textual and content-based search in repositories of web application models. ACM Trans. Web 8, 11:1–11:47 (2014)CrossRefGoogle Scholar
  8. 8.
    Bourque, P., Dupuis, R., Abran, A., Moore, J.W., Tripp, L.L.: The guide to the software engineering body of knowledge. IEEE Softw. 16, 35–44 (1999)CrossRefGoogle Scholar
  9. 9.
    Anquetil, N., Fourrier, C., Lethbridge, T.C.: Experiments with clustering as a software remodularization method. In: Proceedings of the Sixth Working Confernce on Reverse Engineering, WCRE 1999, pp. 235–255. IEEE Computer Society (1999)Google Scholar
  10. 10.
    Beck, F., Diehl, S.: On the impact of software evolution on software clustering. Empirical Softw. Eng. 18, 970–1004 (2012)CrossRefGoogle Scholar
  11. 11.
    Vanya, A., Holland, L., Klusener, S., van de Laar, P., van Vliet, H.: Assessing software archives with evolutionary clusters. In: 16th International Conference on Program Comprehension, pp. 192–201. IEEE (2008)Google Scholar
  12. 12.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31, 264–323 (1999)CrossRefGoogle Scholar
  13. 13.
    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 CloudMDE at MoDELS, pp. 66–75(2014)Google Scholar
  14. 14.
    Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidleberg (2006)CrossRefGoogle Scholar
  15. 15.
    Steinbach, M., Ertöz, L., Kumar, V.: The challenges of clustering high dimensional data. In: Wille, L.T. (ed.) New Directions in Statistical Physics, pp. 273–309. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, London (2006). Chapter 8Google Scholar
  17. 17.
    Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Collaborative repositories in model-driven engineering. IEEE Softw. 32(3), 28–34 (2015)CrossRefGoogle Scholar
  18. 18.
    Gomes, C., Barroca, B., Amaral, V.: Classification of model transformation tools: pattern matching techniques. In: Dingel, J., Schulte, W., Ramos, I., Abrahão, S., Insfran, E. (eds.) MODELS 2014. LNCS, vol. 8767, pp. 619–635. Springer, Heidelberg (2014)Google Scholar
  19. 19.
    Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Mining correlations of ATL model transformation and metamodel metrics. In: Proceedings of the Seventh International Workshop on Modeling in Software Engineering, MiSE 2015 - ICSE, pp. 54–59. IEEE Press (2015)Google Scholar
  20. 20.
    Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Mining metrics for understanding metamodel characteristics. In: 6th International Workshop on Modeling in Software Engineering, MiSE 2014 - ICSE, Hyderabad, India, 2–3 June 2014, pp. 55–60 (2014)Google Scholar
  21. 21.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar
  22. 22.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001)CrossRefMATHGoogle Scholar
  23. 23.
    El Beggar, O., Bousetta, B., Taoufiq, G.: Comparative study between clustering and model driven reverse engineering approaches. Lect. Notes Softw. Eng. 1(2) (2013)Google Scholar
  24. 24.
    Kawaguchi, S., Garg, P.K., Matsushita, M., Inoue, K.: Mudablue: an automatic categorization system for open source repositories. J. Syst. Softw. 79, 939–953 (2006)CrossRefGoogle Scholar
  25. 25.
    Missaoui, R., Godin, R., Sahraoui, H.: Migrating to an object-oriented database using semantic clustering and transformation rules. Data Knowl. Eng. 27, 97–113 (1998)CrossRefMATHGoogle Scholar
  26. 26.
    Strüber, D., Selter, M., Taentzer, G.: Tool support for clustering large meta-models. In: Proceedings of the Workshop on Scalability in Model Driven Engineering, BigMDE 2013 at STAF, pp. 7: 1–7: 4. ACM (2013)Google Scholar
  27. 27.
    Lopez, O., Laguna, M.A., Garcia, F.J.: Reuse based analysis and clustering of requirements diagrams. In: Eighth International Workshop on Requirements Engineering: Foundation for Software Quality (REFSQ02), pp. 71–82 (2002)Google Scholar
  28. 28.
    Chen, K., Zhang, W., Zhao, H., Mei, H.: An approach to constructing feature models based on requirements clustering. In: Proceedings of 13th IEEE International Conference on Requirements Engineering, pp. 31–40 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francesco Basciani
    • 1
  • Juri Di Rocco
    • 1
  • Davide Di Ruscio
    • 1
  • Ludovico Iovino
    • 2
  • Alfonso Pierantonio
    • 1
  1. 1.Department of Information Engineering, Computer Science and MathematicsUniversità degli Studi dell’AquilaL’AquilaItaly
  2. 2.Gran Sasso Science InstituteL’AquilaItaly

Personalised recommendations