A Variability-Based Approach to Reusable and Efficient Model Transformations

  • Daniel StrüberEmail author
  • Julia Rubin
  • Marsha Chechik
  • Gabriele Taentzer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9033)


Large model transformation systems often contain transformation rules that are substantially similar to each other, causing performance bottlenecks for systems in which rules are applied nondeterministically, as long as one of them is applicable. We tackle this problem by introducing variability-based graph transformations. We formally define variability-based rules and contribute a novel match-finding algorithm for applying them. We prove correctness of our approach by showing its equivalence to the classic one of applying the rules individually, and demonstrate the achieved performance speed-up on a realistic transformation scenario.


Base Rule Model Transformation Transformation Rule Variation Point Object Constraint Language 
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.


  1. 1.
    Kusel, A., Schönböck, J., Wimmer, M., Kappel, G., Retschitzegger, W., Schwinger, W.: Reuse in Model-to-Model Transformation Languages: Are We There Yet? In: SoSyM, pp. 1–36 (2013)Google Scholar
  2. 2.
    Soley, R.: Model Driven Architecture. Object Management Group (2000)Google Scholar
  3. 3.
    Clements, P.C., Northrop, L.: Software Product Lines: Practices and Patterns. Addison-Wesley (2001)Google Scholar
  4. 4.
    Pohl, K., Boeckle, G., van der Linden, F.: Software Product Line Engineering: Foundations, Principles, and Techniques. Springer (2005)Google Scholar
  5. 5.
    Sijtema, M.: Introducing Bariability Rules in ATL for Managing Variability in MDE-based Product Lines. In: Proc. of MtATL 2010, pp. 39–49 (2010)Google Scholar
  6. 6.
    Kavimandan, A., Gokhale, A., Karsai, G., Gray, J.: Managing the Quality of Software Product Line Architectures through Reusable Model Transformations. In: Proc. of QoSA/ISARCS 2011, pp. 13–22. ACM (2011)Google Scholar
  7. 7.
    Trujillo, S., Zubizarreta, A., De Sosa, J., Mendialdua, X.: On the Refinement of Model-to-Text Transformations. In: Proc. of JISBD 2009, pp. 123–133 (2009)Google Scholar
  8. 8.
    Ehrig, H., Ehrig, K., Prange, U., Taentzer, G.: Fundamental Theory for Typed Attributed Graphs and Graph Transformation based on Adhesive HLR Categories. Fundamenta Informatica 74, 31–61 (2006)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Arendt, T., Habel, A., Radke, H., Taentzer, G.: From Core OCL Invariants to Nested Graph Constraints. In: Giese, H., König, B. (eds.) ICGT 2014. LNCS, vol. 8571, pp. 97–112. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  10. 10.
    Strüber, D., Rubin, J., Chechik, M., Taentzer, G.: A Variability-Based Approach to Reusable and Efficient Model Transformation - Technical Report,
  11. 11.
    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, Part I. LNCS, vol. 6394, pp. 121–135. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kenner, A., Kästner, C., Haase, S., Leich, T.: TypeChef: Toward Type Checking #ifdef Variability in C. In: Proc. of FOSD 2010, pp. 25–32 (2010)Google Scholar
  13. 13.
    Czarnecki, K., Antkiewicz, M.: Mapping Features to Models: A Template Approach Based on Superimposed Variants. In: Glück, R., Lowry, M. (eds.) GPCE 2005. LNCS, vol. 3676, pp. 422–437. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Kästner, C., Apel, S.: Integrating Compositional and Annotative Approaches for Product Line Engineering. In: Proc. of the Wksp. on Modularization, Composition and Generative Techniques for PLE (McGPLE) at GPCE 2008, pp. 35–40 (2008)Google Scholar
  15. 15.
    Rubin, J., Chechik, M.: Combining related products into product lines. In: de Lara, J., Zisman, A. (eds.) Fundamental Approaches to Software Engineering. LNCS, vol. 7212, pp. 285–300. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Anjorin, A., Saller, K., Lochau, M., Schürr, A.: Modularizing Triple Graph Grammars Using Rule Refinement. In: Gnesi, S., Rensink, A. (eds.) FASE 2014 (ETAPS). LNCS, vol. 8411, pp. 340–354. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Jouault, F., Allilaire, F., Bézivin, J., Kurtev, I., Valduriez, P.: Atl: A qvt-like transformation language. In: Companion to the 21st ACM SIGPLAN Symposium on Object-Oriented Programming Systems, Languages, and Applications, pp. 719–720. ACM (2006)Google Scholar
  18. 18.
    Cuadrado, J.S., Molina, J.G.: A Model-Based Approach to Families of Embedded Domain-Specific Languages. IEEE TSE 35, 825–840 (2009)Google Scholar
  19. 19.
    Salay, R., Famelis, M., Rubin, J., Sandro, A.D., Chechik, M.: Lifting Model Transformations to Product Lines. In: Proc. of ICSE 2014, pp. 117–128 (2014)Google Scholar
  20. 20.
    Sánchez Cuadrado, J., Guerra, E., de Lara, J.: Reverse engineering of model transformations for reusability. In: Di Ruscio, D., Varró, D. (eds.) ICMT 2014. LNCS, vol. 8568, pp. 186–201. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  21. 21.
    Bergmann, G., Ráth, I., Szabó, T., Torrini, P., Varró, D.: Incremental pattern matching for the efficient computation of transitive closure. In: Ehrig, H., Engels, G., Kreowski, H.-J., Rozenberg, G. (eds.) ICGT 2012. LNCS, vol. 7562, pp. 386–400. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Strüber, D., Taentzer, G., Jurack, S., Schäfer, T.: Towards a distributed modeling process based on composite models. In: Cortellessa, V., Varró, D. (eds.) FASE 2013 (ETAPS 2013). LNCS, vol. 7793, pp. 6–20. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Daniel Strüber
    • 1
    Email author
  • Julia Rubin
    • 2
  • Marsha Chechik
    • 3
  • Gabriele Taentzer
    • 1
  1. 1.Philipps-Universität MarburgMarburgGermany
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.University of TorontoTorontoCanada

Personalised recommendations