Advertisement

Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models

  • Dániel Varró
  • Oszkár SemeráthEmail author
  • Gábor Szárnyas
  • Ákos Horváth
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10800)

Abstract

Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time.

We provide foundations for a class of model generators along a refinement relation which operates over partial models with 3-valued representation and ensures that subsequently derived partial models preserve the truth evaluation of well-formedness constraints in the domain. We formally prove completeness, i.e. any finite instance model of a domain can be generated by model generator transformations in finite steps and soundness, i.e. any instance model retrieved as a solution satisfies all well-formedness constraints. An experimental evaluation is carried out in the context of a statechart modeling tool to evaluate the trade-off between different characteristics of model generators.

Keywords

Automated model generation Partial models Refinement 

Notes

Acknowledgements

The authors are really grateful for the anonymous reviewers and Szilvia Varró-Gyapay for the numerous constructive feedback to improve the current paper. This paper is partially supported by MTA-BME Lendület Research Group on Cyber-Physical Systems, and NSERC RGPIN-04573-16 project.

References

  1. 1.
  2. 2.
  3. 3.
    Al-Sibahi, A.S., Dimovski, A.S., Wasowski, A.: Symbolic execution of high-level transformations. In: Proceedings of the 2016 ACM SIGPLAN International Conference on Software Language Engineering, Amsterdam, 31 October–1 November 2016, pp. 207–220 (2016). http://dl.acm.org/citation.cfm?id=2997382
  4. 4.
    Ali, S., Iqbal, M.Z.Z., Arcuri, A., Briand, L.C.: Generating test data from OCL constraints with search techniques. IEEE Trans. Softw. Eng. 39(10), 1376–1402 (2013)CrossRefGoogle Scholar
  5. 5.
    Anastasakis, K., Bordbar, B., Georg, G., Ray, I.: On challenges of model transformation from UML to Alloy. Softw. Syst. Model. 9(1), 69–86 (2010)CrossRefGoogle Scholar
  6. 6.
    Aranega, V., Mottu, J.M., Etien, A., Degueule, T., Baudry, B., Dekeyser, J.L.: Towards an automation of the mutation analysis dedicated to model transformation. Softw. Test. Verif. Reliab. 25(5–7), 653–683 (2015)CrossRefGoogle Scholar
  7. 7.
    Bagan, G., Bonifati, A., Ciucanu, R., Fletcher, G.H.L., Lemay, A., Advokaat, N.: gMark: schema-driven generation of graphs and queries. IEEE Trans. Knowl. Data Eng. 29(4), 856–869 (2017)CrossRefGoogle Scholar
  8. 8.
    Bak, K., Diskin, Z., Antkiewicz, M., Czarnecki, K., Wasowski, A.: Clafer: unifying class and feature modeling. Softw. Syst. Model. 15(3), 811–845 (2016)CrossRefGoogle Scholar
  9. 9.
    Batot, E., Sahraoui, H.: A generic framework for model-set selection for the unification of testing and learning MDE tasks. In: MODELS. pp. 374–384. ACM Press (2016)Google Scholar
  10. 10.
    Battiston, F., Nicosia, V., Latora, V.: Structural measures for multiplex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 89(3), 032804 (2014)CrossRefGoogle Scholar
  11. 11.
    Bergmann, G., Ujhelyi, Z., Ráth, I., Varró, D.: A graph query language for EMF models. In: Cabot, J., Visser, E. (eds.) ICMT 2011. LNCS, vol. 6707, pp. 167–182. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21732-6_12 CrossRefGoogle Scholar
  12. 12.
    Berlingerio, M., et al.: Multidimensional networks: foundations of structural analysis. World Wide Web 16(5–6), 567–593 (2013)CrossRefGoogle Scholar
  13. 13.
    Bizer, C., Schultz, A.: The Berlin SPARQL benchmark. Int. J. Sem. Web Inf. Syst. 5(2), 1–24 (2009)CrossRefGoogle Scholar
  14. 14.
    Boyapati, C., Khurshid, S., Marinov, D.: Korat: automated testing based on Java predicates. In: International Symposium on Software Testing and Analysis (ISSTA), pp. 123–133. ACM Press (2002)Google Scholar
  15. 15.
    Brottier, E., Fleurey, F., Steel, J., Baudry, B., Le Traon, Y.: Metamodel-based test generation for model transformations: an algorithm and a tool. In: ISSRE, pp. 85–94, November 2006Google Scholar
  16. 16.
    Bures, T., et al.: Software engineering for smart cyber-physical systems - towards a research agenda. ACM SIGSOFT Softw. Eng. Notes 40(6), 28–32 (2015)CrossRefGoogle Scholar
  17. 17.
    Büttner, F., Egea, M., Cabot, J., Gogolla, M.: Verification of ATL transformations using transformation models and model finders. In: Aoki, T., Taguchi, K. (eds.) ICFEM 2012. LNCS, vol. 7635, pp. 198–213. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34281-3_16 CrossRefGoogle Scholar
  18. 18.
    Cabot, J., Clarisó, R., Riera, D.: On the verification of UML/OCL class diagrams using constraint programming. J. Syst. Softw. 93, 1–23 (2014)CrossRefGoogle Scholar
  19. 19.
    Cabot, J., Teniente, E.: Incremental integrity checking of UML/OCL conceptual schemas. J. Syst. Softw. 82(9), 1459–1478 (2009)CrossRefGoogle Scholar
  20. 20.
    Clavel, M., Egea, M., de Dios, M.A.G.: Checking unsatisfiability for OCL constraints. ECEASST, vol. 24 (2009)Google Scholar
  21. 21.
    Corradini, A., König, B., Nolte, D.: Specifying graph languages with type graphs. In: de Lara, J., Plump, D. (eds.) ICGT 2017. LNCS, vol. 10373, pp. 73–89. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61470-0_5 CrossRefGoogle Scholar
  22. 22.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal Complex Syst. 1695 (2006). http://igraph.sf.net
  23. 23.
    Cunha, A., Garis, A., Riesco, D.: Translating between alloy specifications and UML class diagrams annotated with OCL. Softw. Syst. Model. 14(1), 5–25 (2015)CrossRefGoogle Scholar
  24. 24.
    Czarnecki, K., Pietroszek, K.: Verifying feature-based model templates against well-formedness OCL constraints. In: 5th International Conference on Generative Programming and Component Engineering, GPCE 2006, pp. 211–220. ACM (2006)Google Scholar
  25. 25.
    Darabos, A., Pataricza, A., Varró, D.: Towards testing the implementation of graph transformations. In: GTVMT. ENTCS. Elsevier (2006)Google Scholar
  26. 26.
    DeWitt, D.J.: The Wisconsin benchmark: past, present, and future. In: The Benchmark Handbook, pp. 119–165 (1991)Google Scholar
  27. 27.
    Duchon, P., Flajolet, P., Louchard, G., Schaeffer, G.: Boltzmann samplers for the random generation of combinatorial structures. Comb. Probab. Comput. 13(4–5), 577–625 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Ehrig, H., Ehrig, K., Prange, U., Taentzer, G.: Fundamentals of Algebraic Graph Transformation. Monographs in Theoretical Computer Science. An EATCS Series. Springer, Heidelberg (2006).  https://doi.org/10.1007/3-540-31188-2 zbMATHGoogle Scholar
  29. 29.
    Ehrig, K., Küster, J.M., Taentzer, G.: Generating instance models from meta models. Softw. Syst. Model. 8(4), 479–500 (2009)CrossRefGoogle Scholar
  30. 30.
    Erling, O., et al.: The LDBC social network benchmark: interactive workload. In: SIGMOD, pp. 619–630 (2015)Google Scholar
  31. 31.
    Famelis, M., Salay, R., Chechik, M.: Partial models: towards modeling and reasoning with uncertainty. In: ICSE, pp. 573–583. IEEE Press (2012)Google Scholar
  32. 32.
    Famelis, M., Salay, R., Chechik, M.: The semantics of partial model transformations. In: MiSE at ICSE, pp. 64–69. IEEE Press (2012)Google Scholar
  33. 33.
    Famelis, M., Salay, R., Di Sandro, A., Chechik, M.: Transformation of models containing uncertainty. In: Moreira, A., Schätz, B., Gray, J., Vallecillo, A., Clarke, P. (eds.) MODELS 2013. LNCS, vol. 8107, pp. 673–689. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41533-3_41 CrossRefGoogle Scholar
  34. 34.
    Fleurey, F., Baudry, B., Muller, P.A., Le Traon, Y.: Towards dependable model transformations: qualifying input test data, appears to be published only in a technical report by INRIA (2007). https://hal.inria.fr/inria-00477567
  35. 35.
    Gogolla, M., Büttner, F., Richters, M.: USE: a UML-based specification environment for validating UML and OCL. Sci. Comput. Program. 69(1–3), 27–34 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Goldberg, A.P., Chew, Y.H., Karr, J.R.: Toward scalable whole-cell modeling of human cells. In: SIGSIM-PADS, pp. 259–262. ACM Press (2016)Google Scholar
  37. 37.
    González, C.A., Cabot, J.: ATLTest: a white-box test generation approach for ATL transformations. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 449–464. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33666-9_29 CrossRefGoogle Scholar
  38. 38.
    González, C.A., Cabot, J.: Test data generation for model transformations combining partition and constraint analysis. In: Di Ruscio, D., Varró, D. (eds.) ICMT 2014. LNCS, vol. 8568, pp. 25–41. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08789-4_3 Google Scholar
  39. 39.
    Guerra, E., Soeken, M.: Specification-driven model transformation testing. Softw. Syst. Model. 14(2), 623–644 (2015)CrossRefGoogle Scholar
  40. 40.
    Habel, A., Pennemann, K.-H.: Nested constraints and application conditions for high-level structures. In: Kreowski, H.-J., Montanari, U., Orejas, F., Rozenberg, G., Taentzer, G. (eds.) Formal Methods in Software and Systems Modeling. LNCS, vol. 3393, pp. 293–308. Springer, Heidelberg (2005).  https://doi.org/10.1007/978-3-540-31847-7_17 CrossRefGoogle Scholar
  41. 41.
    Habel, A., Pennemann, K.: Correctness of high-level transformation systems relative to nested conditions. Math. Struct. Comput. Sci. 19(2), 245–296 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Härtel, J., Härtel, L., Lämmel, R.: Test-data generation for Xtext. In: Combemale, B., Pearce, D.J., Barais, O., Vinju, J.J. (eds.) SLE 2014. LNCS, vol. 8706, pp. 342–351. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11245-9_19 Google Scholar
  43. 43.
    ISO: Road vehicles - functional safety (ISO 26262) (2011)Google Scholar
  44. 44.
    Izsó, B., Szatmári, Z., Bergmann, G., Horváth, Á., Ráth, I.: Towards precise metrics for predicting graph query performance. In: ASE, pp. 421–431 (2013)Google Scholar
  45. 45.
    Jackson, D.: Alloy: a lightweight object modelling notation. ACM Trans. Softw. Eng. Methodol. 11(2), 256–290 (2002)CrossRefGoogle Scholar
  46. 46.
    Jackson, E.K., Levendovszky, T., Balasubramanian, D.: Automatically reasoning about metamodeling. Softw. Syst. Model. 14(1), 271–285 (2015)CrossRefGoogle Scholar
  47. 47.
    Jackson, E.K., Simko, G., Sztipanovits, J.: Diversely enumerating system-level architectures. In: EMSOFT, p. 11. IEEE Press (2013)Google Scholar
  48. 48.
    Kleene, S.C., De Bruijn, N., de Groot, J., Zaanen, A.C.: Introduction to Metamathematics, vol. 483. van Nostrand, New York (1952)Google Scholar
  49. 49.
    Kolovos, D.S., Paige, R.F., Polack, F.A.C.: On the evolution of OCL for capturing structural constraints in modelling languages. In: Abrial, J.-R., Glässer, U. (eds.) Rigorous Methods for Software Construction and Analysis. LNCS, vol. 5115, pp. 204–218. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-11447-2_13 CrossRefGoogle Scholar
  50. 50.
    Kuhlmann, M., Gogolla, M.: From UML and OCL to relational logic and back. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 415–431. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33666-9_27 CrossRefGoogle Scholar
  51. 51.
    Kuhlmann, M., Gogolla, M.: Strengthening SAT-based validation of UML/OCL models by representing collections as relations. In: Vallecillo, A., Tolvanen, J.-P., Kindler, E., Störrle, H., Kolovos, D. (eds.) ECMFA 2012. LNCS, vol. 7349, pp. 32–48. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31491-9_5 CrossRefGoogle Scholar
  52. 52.
    Kuhlmann, M., Hamann, L., Gogolla, M.: Extensive validation of OCL models by integrating SAT solving into USE. In: Bishop, J., Vallecillo, A. (eds.) TOOLS 2011. LNCS, vol. 6705, pp. 290–306. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21952-8_21 CrossRefGoogle Scholar
  53. 53.
    Le Berre, D., Parrain, A.: The Sat4j library, release 2.2. J. Satisf. Boolean Model. Comput. 7, 59–64 (2010)Google Scholar
  54. 54.
    Lee, E.A., et al.: The swarm at the edge of the cloud. IEEE Des. Test 31(3), 8–20 (2014)CrossRefGoogle Scholar
  55. 55.
    Lehmann, E.L., D’Abrera, H.J.: Nonparametrics: Statistical Methods Based on Ranks. Springer, New York (2006)zbMATHGoogle Scholar
  56. 56.
    López-Fernández, J.J., Guerra, E., de Lara, J.: Combining unit and specification-based testing for meta-model validation and verification. Inf. Syst. 62, 104–135 (2016)CrossRefGoogle Scholar
  57. 57.
    Meedeniya, I., Aleti, A., Grunske, L.: Architecture-driven reliability optimization with uncertain model parameters. J. Syst. Softw. 85(10), 2340–2355 (2012)CrossRefGoogle Scholar
  58. 58.
    Micskei, Z., Szatmári, Z., Oláh, J., Majzik, I.: A concept for testing robustness and safety of the context-aware behaviour of autonomous systems. In: Jezic, G., Kusek, M., Nguyen, N.-T., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2012. LNCS (LNAI), vol. 7327, pp. 504–513. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-30947-2_55 CrossRefGoogle Scholar
  59. 59.
    Misailovic, S., Milicevic, A., Petrovic, N., Khurshid, S., Marinov, D.: Parallel test generation and execution with Korat. In: ESEC-FSE 2007, pp. 135–144. ACM (2007)Google Scholar
  60. 60.
    Morsey, M., Lehmann, J., Auer, S., Ngonga Ngomo, A.-C.: DBpedia SPARQL benchmark – performance assessment with real queries on real data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 454–469. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_29 CrossRefGoogle Scholar
  61. 61.
    Mottu, J.-M., Baudry, B., Le Traon, Y.: Mutation analysis testing for model transformations. In: Rensink, A., Warmer, J. (eds.) ECMDA-FA 2006. LNCS, vol. 4066, pp. 376–390. Springer, Heidelberg (2006).  https://doi.org/10.1007/11787044_28 CrossRefGoogle Scholar
  62. 62.
    Mottu, J.M., Sen, S., Tisi, M., Cabot, J.: Static analysis of model transformations for effective test generation. In: ISSRE, pp. 291–300. IEEE, November 2012Google Scholar
  63. 63.
    Mottu, J.M., Simula, S.S., Cadavid, J., Baudry, B.: Discovering model transformation pre-conditions using automatically generated test models. In: ISSRE, pp. 88–99. IEEE, November 2015Google Scholar
  64. 64.
    Mougenot, A., Darrasse, A., Blanc, X., Soria, M.: Uniform random generation of huge metamodel instances. In: Paige, R.F., Hartman, A., Rensink, A. (eds.) ECMDA-FA 2009. LNCS, vol. 5562, pp. 130–145. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02674-4_10 CrossRefGoogle Scholar
  65. 65.
    de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-78800-3_24 CrossRefGoogle Scholar
  66. 66.
    Neema, S., Sztipanovits, J., Karsai, G., Butts, K.: Constraint-based design-space exploration and model synthesis. In: Alur, R., Lee, I. (eds.) EMSOFT 2003. LNCS, vol. 2855, pp. 290–305. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-45212-6_19 CrossRefGoogle Scholar
  67. 67.
    Nickel, U., Niere, J., Zündorf, A.: The FUJABA environment. In: ICSE, pp. 742–745. ACM (2000)Google Scholar
  68. 68.
    Nicosia, V., Latora, V.: Measuring and modeling correlations in multiplex networks. Phys. Rev. E 92, 032805 (2015)CrossRefGoogle Scholar
  69. 69.
    Nielsen, C.B., Larsen, P.G., Fitzgerald, J.S., Woodcock, J., Peleska, J.: Systems of systems engineering: basic concepts, model-based techniques, and research directions. ACM Comput. Surv. 48(2), 18 (2015)CrossRefGoogle Scholar
  70. 70.
    The Object Management Group: Object Constraint Language, v2.0, May 2006Google Scholar
  71. 71.
    Pennemann, K.-H.: Resolution-like theorem proving for high-level conditions. In: Ehrig, H., Heckel, R., Rozenberg, G., Taentzer, G. (eds.) ICGT 2008. LNCS, vol. 5214, pp. 289–304. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-87405-8_20 CrossRefGoogle Scholar
  72. 72.
    Pham, M.-D., Boncz, P., Erling, O.: S3G2: a scalable structure-correlated social graph generator. In: Nambiar, R., Poess, M. (eds.) TPCTC 2012. LNCS, vol. 7755, pp. 156–172. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36727-4_11 CrossRefGoogle Scholar
  73. 73.
    Przigoda, N., Hilken, F., Peters, J., Wille, R., Gogolla, M., Drechsler, R.: Integrating an SMT-based ModelFinder into USE. In: Model-Driven Engineering, Verification and Validation (MoDeVVa) at MODELS, vol. 1713, pp. 40–45 (2016)Google Scholar
  74. 74.
    Queralt, A., Artale, A., Calvanese, D., Teniente, E.: OCL-Lite: finite reasoning on UML/OCL conceptual schemas. Data Knowl. Eng. 73, 1–22 (2012)CrossRefGoogle Scholar
  75. 75.
    Rensink, A., Distefano, D.: Abstract graph transformation. Electr. Notes in Theoret. Comp. Sci. 157(1), 39–59 (2006)CrossRefzbMATHGoogle Scholar
  76. 76.
    Reps, T.W., Sagiv, M., Wilhelm, R.: Static program analysis via 3-valued logic. In: Alur, R., Peled, D.A. (eds.) CAV 2004. LNCS, vol. 3114, pp. 15–30. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27813-9_2 CrossRefGoogle Scholar
  77. 77.
    Salay, R., Chechik, M., Famelis, M., Gorzny, J.: A methodology for verifying refinements of partial models. J. Object Technol. 14(3), 3:1–3:31 (2015)CrossRefGoogle Scholar
  78. 78.
    Salay, R., Chechik, M., Gorzny, J.: Towards a methodology for verifying partial model refinements. In: ICST, pp. 938–945. IEEE (2012)Google Scholar
  79. 79.
    Salay, R., Famelis, M., Chechik, M.: Language independent refinement using partial modeling. In: de Lara, J., Zisman, A. (eds.) FASE 2012. LNCS, vol. 7212, pp. 224–239. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28872-2_16 CrossRefGoogle Scholar
  80. 80.
    Schmidt, M., Hornung, T., Lausen, G., Pinkel, C.: SP2Bench: a SPARQL performance benchmark. In: ICDE, pp. 222–233. IEEE (2009)Google Scholar
  81. 81.
    Schneider, S., Lambers, L., Orejas, F.: Symbolic model generation for graph properties. In: Huisman, M., Rubin, J. (eds.) FASE 2017. LNCS, vol. 10202, pp. 226–243. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-662-54494-5_13 CrossRefGoogle Scholar
  82. 82.
    Schölzel, H., Ehrig, H., Maximova, M., Gabriel, K., Hermann, F.: Satisfaction, restriction and amalgamation of constraints in the framework of M-adhesive categories. In: Proceedings Seventh ACCAT Workshop on Applied and Computational Category Theory, ACCAT 2012, Tallinn, 1 April 2012. EPTCS, vol. 93, pp. 83–104 (2012)Google Scholar
  83. 83.
    Schonbock, J., Kappel, G., Wimmer, M., Kusel, A., Retschitzegger, W., Schwinger, W.: TETRABox - a generic white-box testing framework for model transformations. In: APSEC, pp. 75–82. IEEE, December 2013Google Scholar
  84. 84.
    Semeráth, O., Barta, Á., Horváth, Á., Szatmári, Z., Varró, D.: Formal validation of domain-specific languages with derived features and well-formedness constraints. Softw. Syst, Model. 16(2), 357–392 (2017)Google Scholar
  85. 85.
    Semeráth, O., Varró, D.: Graph constraint evaluation over partial models by constraint rewriting. In: Guerra, E., van den Brand, M. (eds.) ICMT 2017. LNCS, vol. 10374, pp. 138–154. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61473-1_10 CrossRefGoogle Scholar
  86. 86.
    Semeráth, O., Vörös, A., Varró, D.: Iterative and incremental model generation by logic solvers. In: Stevens, P., Wąsowski, A. (eds.) FASE 2016. LNCS, vol. 9633, pp. 87–103. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49665-7_6 CrossRefGoogle Scholar
  87. 87.
    Sen, S., Baudry, B., Mottu, J.M.: On combining multi-formalism knowledge to select models for model transformation testing. In: ICST, pp. 328–337. IEEE (2008)Google Scholar
  88. 88.
    Spasic, M., Jovanovik, M., Prat-Pérez, A.: An RDF dataset generator for the social network benchmark with real-world coherence. In: BLINK (2016)Google Scholar
  89. 89.
    RTCA: DO-178C, software considerations in airborne systems and equipment certification (2012). Technical reportGoogle Scholar
  90. 90.
    Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF: Eclipse Modeling Framework 2.0, 2nd edn. Addison-Wesley Professional, Reading (2009)Google Scholar
  91. 91.
    Szárnyas, G., Kővári, Z., Salánki, Á., Varró, D.: Towards the characterization of realistic models: evaluation of multidisciplinary graph metrics. In: MODELS, 87–94 (2016)Google Scholar
  92. 92.
    Szárnyas, G., Izsó, B., Ráth, I., Varró, D.: The train benchmark: cross-technology performance evaluation of continuous model queries. Softw. Syst. Model. (2017). https://doi.org/10.1007/s10270-016-0571-8
  93. 93.
    Sztipanovits, J., Koutsoukos, X., Karsai, G., Kottenstette, N., Antsaklis, P., Gupta, V., Goodwine, B., Baras, J.: Toward a science of cyber-physical system integration. Proc. IEEE 100(1), 29–44 (2012)CrossRefGoogle Scholar
  94. 94.
    Torlak, E., Jackson, D.: Kodkod: a relational model finder. In: Grumberg, O., Huth, M. (eds.) TACAS 2007. LNCS, vol. 4424, pp. 632–647. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-71209-1_49 CrossRefGoogle Scholar
  95. 95.
    Torrini, P., Heckel, R., Ráth, I.: Stochastic simulation of graph transformation systems. In: Rosenblum, D.S., Taentzer, G. (eds.) FASE 2010. LNCS, vol. 6013, pp. 154–157. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12029-9_11 CrossRefGoogle Scholar
  96. 96.
    Ujhelyi, Z., Bergmann, G., Hegedüs, Á., Horváth, Á., Izsó, B., Ráth, I., Szatmári, Z., Varró, D.: EMF-IncQuery: an integrated development environment for live model queries. Sci. Comput. Program. 98, 80–99 (2015)CrossRefGoogle Scholar
  97. 97.
    Varró, D., Bergmann, G., Hegedüs, Á., Horváth, Á., Ráth, I., Ujhelyi, Z.: Road to a reactive and incremental model transformation platform: three generations of the VIATRA framework. Softw. Syst. Model. 15(3), 609–629 (2016)CrossRefGoogle Scholar
  98. 98.
    Varró, D., Balogh, A.: The model transformation language of the VIATRA2 framework. Sci. Comput. Program. 68(3), 214–234 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  99. 99.
    Waltemath, D., et al.: Toward community standards and software for whole-cell modeling. IEEE Trans. Bio-med. Eng. 63(10), 2007–2014 (2016)CrossRefGoogle Scholar
  100. 100.
    Winkelmann, J., Taentzer, G., Ehrig, K., Küster, J.M.: Translation of restricted OCL constraints into graph constraints for generating meta model instances by graph grammars. Electr. Notes Theor. Comput. Sci. 211, 159–170 (2008)CrossRefzbMATHGoogle Scholar
  101. 101.
    Yakindu Statechart Tools: Yakindu. http://statecharts.org/
  102. 102.
    Zhang, J.W., Tay, Y.C.: GSCALER: synthetically scaling a given graph. In: EDBT, pp. 53–64 (2016).  https://doi.org/10.5441/002/edbt.2016.08

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Budapest University of Technology and EconomicsBudapestHungary
  2. 2.MTA-BME Lendület Research Group on Cyber-Physical SystemsBudapestHungary
  3. 3.Department of Electrical and Computer EngineeringMcGill UniversityMontrealCanada
  4. 4.IncQuery Labs Ltd.BudapestHungary

Personalised recommendations