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Automated Chaining of Model Transformations with Incompatible Metamodels

  • Francesco Basciani
  • Davide Di Ruscio
  • Ludovico Iovino
  • Alfonso Pierantonio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8767)

Abstract

In Model-Driven Engineering (MDE) models are first-class entities that are manipulated by means of model transformations. The development of complex and large transformations can benefit from the reuse of smaller ones that can be composed according to user requirements. Composing transformations is a complex problem: typically smaller transformations are discovered and selected by developers from different and heterogeneous sources. Then the identified transformations are chained by means of manual and error-prone composition processes.

In this paper we propose an approach to automatically discover and compose transformations: developers provide the system with the source models and specify the target metamodel. By relying on a repository of model transformations, all the possible transformation chains are calculated. Importantly, in case of incompatible intermediate target and source metamodels, proper adapters are automatically generated in order to chain also transformations that otherwise would be discarded by limiting the reuse possibilities of available transformations.

Keywords

Model Transformation User Request Couple Evolution Transformation Chain Delta Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Etien, A., Aranega, V., Blanc, X., Paige, R.F.: Chaining Model Transformations. In: Proceedings of the First Workshop on the Analysis of Model Transformations, AMT 2012, pp. 9–14. ACM, New York (2012)Google Scholar
  2. 2.
    Etien, A., Muller, A., Legrand, T., Blanc, X.: Combining Independent Model Transformations. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 2237–2243. ACM, New York (2010)Google Scholar
  3. 3.
    Vanhooff, B., Van Baelen, S., Hovsepyan, A., Joosen, W., Berbers, Y.: Towards a Transformation Chain Modeling Language. In: Vassiliadis, S., Wong, S., Hämäläinen, T.D. (eds.) SAMOS 2006. LNCS, vol. 4017, pp. 39–48. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Etien, A., Muller, A., Legrand, T., Paige, R.F.: Localized model transformations for building large-scale transformations. Software Systems Modeling, 1–25 (2013)Google Scholar
  5. 5.
    Wagelaar, D.: Composition Techniques for Rule-Based Model Transformation Languages. In: Vallecillo, A., Gray, J., Pierantonio, A. (eds.) ICMT 2008. LNCS, vol. 5063, pp. 152–167. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Di Ruscio, D., Iovino, L., Pierantonio, A.: Coupled Evolution in Model-Driven Engineering. IEEE Software 29, 78–84 (2012)CrossRefGoogle Scholar
  7. 7.
    Cicchetti, A., Di Ruscio, D., Eramo, R., Pierantonio, A.: Automating Co-evolution in Model-Driven Engineering. In: Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference, EDOC 2008, pp. 222–231. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  8. 8.
    Rivera, J.E., Ruiz-Gonzalez, D., Lopez-Romero, F., Bautista, J., Vallecillo, A.: Orchestrating ATL Model Transformations. In: Proc. of MtATL 2009, Nantes, France, pp. 34–46 (2009)Google Scholar
  9. 9.
    Aranega, V., Etien, A., Mosser, S.: Using Feature Model to Build Model Transformation Chains. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 562–578. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Chenouard, R., Jouault, F.: Automatically Discovering Hidden Transformation Chaining Constraints. In: Schürr, A., Selic, B. (eds.) MODELS 2009. LNCS, vol. 5795, pp. 92–106. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Jouault, F., Kurtev, I.: Transforming Models with ATL. In: Bruel, J.-M. (ed.) MoDELS 2005 Workshops. LNCS, vol. 3844, pp. 128–138. Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Rubin, F.: Enumerating all simple paths in a graph. IEEE Transactions on Circuits and Systems 25, 641–642 (1978)CrossRefzbMATHGoogle Scholar
  13. 13.
    Wachsmuth, G.: Metamodel Adaptation and Model Co-adaptation. In: Ernst, E. (ed.) ECOOP 2007. LNCS, vol. 4609, pp. 600–624. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Cicchetti, A., Di Ruscio, D., Pierantonio, A.: A Metamodel Independent Approach to Difference Representation. Journal of Object Technology 6, 165–185 (2007)CrossRefGoogle Scholar
  15. 15.
    Di Ruscio, D., Iovino, L., Pierantonio, A.: Managing the Coupled Evolution of Metamodels and Textual Concrete Syntax Specifications. In: 2013 39th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), pp. 114–121 (2013)Google Scholar
  16. 16.
    Di Ruscio, D., Lämmel, R., Pierantonio, A.: Automated Co-evolution of GMF Editor Models. In: Malloy, B., Staab, S., van den Brand, M. (eds.) SLE 2010. LNCS, vol. 6563, pp. 143–162. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Di Ruscio, D., Iovino, L., Pierantonio, A.: Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ecosystems. In: Ehrig, H., Engels, G., Kreowski, H.-J., Rozenberg, G. (eds.) ICGT 2012. LNCS, vol. 7562, pp. 20–37. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Di Ruscio, D., Iovino, L., Pierantonio, A.: A Methodological Approach for the Coupled Evolution of Metamodels and ATL Transformations. In: Duddy, K., Kappel, G. (eds.) ICMB 2013. LNCS, vol. 7909, pp. 60–75. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Voigt, K.: Structural Graph-based Metamodel Matching. PhD thesis (2011)Google Scholar
  20. 20.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of 18th International Conference on Data Engineering, pp. 117–128 (2002)Google Scholar
  21. 21.
    Falleri, J.-R., Huchard, M., Lafourcade, M., Nebut, C.: Metamodel Matching for Automatic Model Transformation Generation. In: Czarnecki, K., Ober, I., Bruel, J.-M., Uhl, A., Völter, M. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 326–340. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Planas, E., Cabot, J., Gómez, C.: Two Basic Correctness Properties for ATL Transformations: Executability and Coverage. In: 3rd International Workshop on Model Transformation with ATL, Zurich, Suisse (2011)Google Scholar
  23. 23.
    Vignaga, A.: Metrics for Measuring ATL Model Transformations. Technical report (2009)Google Scholar
  24. 24.
    Read, R.C., Corneil, D.G.: The graph isomorphism disease. J. Graph Theory 1, 339–363 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Lin, Y., Gray, J., Jouault, F.: DSMDiff: A Differentiation Tool for Domain-Specific Models. 16, 349–361 (2007), (Special Issue on Model-Driven Development)Google Scholar
  26. 26.
    Bryant, B.R., Gray, J., Mernik, M., Clarke, P.J., France, R.B., Karsai, G.: Challenges and directions in formalizing the semantics of modeling languages. Comput. Sci. Inf. Syst. 8, 225–253 (2011)CrossRefGoogle Scholar
  27. 27.
    Hülsbusch, M., König, B., Rensink, A., Semenyak, M., Soltenborn, C., Wehrheim, H.: Showing Full Semantics Preservation in Model Transformation - A Comparison of Techniques. In: Méry, D., Merz, S. (eds.) IFM 2010. LNCS, vol. 6396, pp. 183–198. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Sheth, A.P., Larson, J.A.: Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. ACM Comput. Surv. 22, 183–236 (1990)CrossRefGoogle Scholar
  29. 29.
    Rivera, J.E., Ruiz-Gonzalez, D., Lopez-Romero, F., Bautista, J., Vallecillo, A.: Orchestrating ATL Model Transformations. In: Proc. of MtATL 2009, Nantes, France, pp. 34–46 (2009)Google Scholar
  30. 30.
    Wagelaar, D., Van Der Straeten, R., Deridder, D.: Module superimposition: A composition technique for rule-based model transformation languages. Software & Systems Modeling 9, 285–309 (2010)CrossRefGoogle Scholar
  31. 31.
    Braun, V., Margaria, T., Weise, C.: Integrating Tools in the ETI Platform. STTT 1, 31–48 (1997)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francesco Basciani
    • 1
  • Davide Di Ruscio
    • 1
  • Ludovico Iovino
    • 1
  • Alfonso Pierantonio
    • 1
  1. 1.Department of Information Engineering Computer Science and MathematicsUniversity of L’AquilaItaly

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