Lightweight Testing of Communication Networks with e-Motions

  • Javier Troya
  • José M. Bautista
  • Fernando López-Romero
  • Antonio Vallecillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6706)


This paper illustrates the use of high-level domain specific models to specify and test some performance properties of complex systems, in particular Communication Networks, using a light-weight approach. By following a Model-Driven Engineering (MDE) approach, we show the benefits of constructing very abstract models of the systems under test, which can then be easily prototyped and analysed to explore their properties. For this purpose we use e-Motions, a language and its supporting toolkit that allows end-user modelling of real-time systems and their analysis in a graphical manner.


Model Transformation Object Constraint Language Graph Transformation Software Product Line Object Management Group 
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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  1. 1.
    Zave, P.: Lightweight modeling of network protocols,
  2. 2.
    Rivera, J.E., Durán, F., Vallecillo, A.: A graphical approach for modeling time-dependent behavior of DSLs. In: Proc. of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2009), pp. 51–55. IEEE Computer Society, Los Alamitos (2009)CrossRefGoogle Scholar
  3. 3.
    Troya, J., Rivera, J.E., Vallecillo, A.: Simulating domain specific visual models by observation. In: Proc. of the Symposium on Theory of Modeling and Simulation (DEVS 2010), Orlando, FL, US (April 2010)Google Scholar
  4. 4.
    Czarnecki, K., Helsen, S.: Classification of model transformation approaches. In: OOPSLA 2003 Workshop on Generative Techniques in the Context of MDA (2003)Google Scholar
  5. 5.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.: All About Maude - A High-Performance Logical Framework. LNCS, vol. 4350. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  6. 6.
    Object Management Group: Object Constraint Language (OCL) Specification. Version 2.2, OMG Document formal/2010-02-01 (February 2010)Google Scholar
  7. 7.
    Roldán, M., Durán, F.: Representing UML models in mOdCL (2008),
  8. 8.
    Rivera, J.E., Durán, F., Vallecillo, A.: On the behavioral semantics of real-time domain specific visual languages. In: Ölveczky, P.C. (ed.) WRLA 2010. LNCS, vol. 6381, pp. 174–190. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Jouault, F., Allilaire, F., Bézivin, J., Kurtev, I.: ATL: A model transformation tool. Science of Computer Programming 72(1-2), 31–39 (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Rivera, J.E., Vallecillo, A., Durán, F.: Formal specification and analysis of domain specific languages using Maude. Simulation: Transactions of the Society for Modeling and Simulation International 85(11/12), 778–792 (2009)CrossRefGoogle Scholar
  11. 11.
    Ölveczky, P., Meseguer, J.: Semantics and pragmatics of Real-Time Maude. Higher-Order and Symbolic Computation 20(1-2), 161–196 (2007)CrossRefzbMATHGoogle Scholar
  12. 12.
  13. 13.
    Jain, M., Dovrolis, C.: End-to-end available bandwidth: measurement methodology, dynamics, and relation with tcp throughput. IEEE/ACM Transactions Networking 11(4), 537–549 (2003)CrossRefGoogle Scholar
  14. 14.
    Carter, R.L., Crovella, M.E.: Measuring bottleneck link speed in packet-switched networks. Perform. Eval. 27-28, 297–318 (1996)CrossRefGoogle Scholar
  15. 15.
    Lindh, T.: Performance management in switched ATM networks. In: Trigila, S., Mullery, A., Campolargo, M., Vanderstraeten, H., Mampaey, M. (eds.) IS&N 1998. LNCS, vol. 1430, pp. 439–450. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Pacifici, G., Stadler, R.: Integrating resource control and performance management in multimedia networks. In: Proc. of the IEEE International Conference on Communications, Seattle, WA, vol. 3, pp. 1541–1545 (1995)Google Scholar
  17. 17.
    Balsamo, S., Marco, A.D., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: A survey. IEEE Trans. on Software Engineering 30(5), 295–310 (2004)CrossRefGoogle Scholar
  18. 18.
    Cortellessa, V., Di Marco, A., Inverardi, P.: Integrating performance and reliability analysis in a non-functional MDA framework. In: Dwyer, M.B., Lopes, A. (eds.) FASE 2007. LNCS, vol. 4422, pp. 57–71. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Tawhid, R., Petriu, D.C.: Integrating performance analysis in the model driven development of software product lines. In: Busch, C., Ober, I., Bruel, J.-M., Uhl, A., Völter, M. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 490–504. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Li, J., Chinneck, J., Woodside, M., Litoiu, M., Iszlai, G.: Performance model driven QoS guarantees and optimization in clouds. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, CLOUD 2009, pp. 15–22. IEEE Computer Society, Vancouver (2009)CrossRefGoogle Scholar
  21. 21.
    OMG: UML Profile for Modeling and Analysis of Real-time and Embedded Systems (MARTE). Object Management Group (June 2008), OMG doc. ptc/08-06-08Google Scholar
  22. 22.
    Marsan, A.: Stochastic petri nets: An elementary introduction. In: Rozenberg, G. (ed.) APN 1989. LNCS, vol. 424, pp. 1–29. Springer, London (1990)CrossRefGoogle Scholar
  23. 23.
    Denning, P.J., Buzen, J.P.: The operational analysis of queueing network models. ACM Comput. Surv. 10, 225–261 (1978)CrossRefzbMATHGoogle Scholar
  24. 24.
    Clark, A., Gilmore, S., Hillston, J., Tribastone, M.: Stochastic process algebras. In: Bernardo, M., Hillston, J. (eds.) SFM 2007. LNCS, vol. 4486, pp. 132–179. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  25. 25.
    Yaron, O., Sidi, M.: Performance and stability of communication networks via robust exponential bounds. IEEE/ACM Transactions on Networking 1, 372–385 (1993)CrossRefGoogle Scholar
  26. 26.
    Heckel, R.: Stochastic analysis of graph transformation systems: A case study in P2P networks. In: Van Hung, D., Wirsing, M. (eds.) Theoretical Aspects of Computing ICTAC 2005. LNCS, vol. 3722, pp. 53–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    de Lara, J., Vangheluwe, H., Mosterman, P.J.: Modelling and analysis of traffic networks based on graph transformation. In: Proceedings of the FORMS/FORMATS 2004 Symposium on Formal Methods for Automation and Safety in Railway and Automotive Systems, Braunschweig, Germany, pp. 120–127 (2004)Google Scholar
  28. 28.
    Burmester, S., Giese, H., Hirsch, M., Schilling, D., Tichy, M.: The Fujaba real-time tool suite: model-driven development of safety-critical, real-time systems. In: ICSE 2005, pp. 670–671. ACM, NY (2006)Google Scholar
  29. 29.
    Gyapay, S., Heckel, R., Varró, D.: Graph transformation with time: Causality and logical clocks. In: Corradini, A., Ehrig, H., Kreowski, H.-J., Rozenberg, G. (eds.) ICGT 2002. LNCS, vol. 2505, pp. 120–134. Springer, Heidelberg (2002)Google Scholar
  30. 30.
    Syriani, E., Vangheluwe, H.: Programmed graph rewriting with time for simulation-based design. In: Vallecillo, A., Gray, J., Pierantonio, A. (eds.) ICMT 2008. LNCS, vol. 5063, pp. 91–106. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  31. 31.
    Boronat, A., Ölveczky, P.C.: Formal real-time model transformations in MOMENT2. In: Rosenblum, D.S., Taentzer, G. (eds.) FASE 2010. LNCS, vol. 6013, pp. 29–43. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  32. 32.
    de Lara, J., Vangheluwe, H.: Automating the transformation-based analysis of visual languages. Formal Aspects of Computing 22(3-4), 297–326 (2010)CrossRefzbMATHGoogle Scholar
  33. 33.
    Viana, A.C., Maag, S., Zaidi, F.: One step forward: Linking wireless self-organizing network validation techniques with formal testing approaches. ACM Comput. Surv. 43, 7:1–7:36 (2011)CrossRefzbMATHGoogle Scholar
  34. 34.
    Girod, L., Elson, J., Cerpa, A., Stathopoulos, T., Ramanathan, N., Estrin, D.: Em*: a software environment for developing and deploying wireless sensor networks. In: Proceedings of the USENIX General Track (2004)Google Scholar
  35. 35.
    Girod, L., Stathopoulos, T., Ramanathan, N., Elson, J., Osterweil, E., Schoellhammer, T., Estrin, D.: A system for simulation, emulation, and deployment of heterogeneous sensor networks. In: Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems, pp. 201–213. ACM Press, New York (2004)CrossRefGoogle Scholar
  36. 36.
    Keshav, S.: Real: A network simulator. Technical report, Berkeley, CA, USA (1988)Google Scholar
  37. 37.
    Ben Abdesslem, F., Iannone, L., Dias de Amorim, M., Obraczka, K., Solis, I., Fdida, S.: A prototyping environment for wireless multihop networks. In: Fdida, S., Sugiura, K. (eds.) AINTEC 2007. LNCS, vol. 4866, pp. 33–47. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  38. 38.
    Agha, G., Meseguer, J., Sen, K.: PMaude: Rewrite-based specification language for probabilistic object systems. Electronic Notes in Theoretical Computer Science 153(2), 213–239 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Javier Troya
    • 1
  • José M. Bautista
    • 1
  • Fernando López-Romero
    • 1
  • Antonio Vallecillo
    • 1
  1. 1.GISUM/Atenea Research Group.Universidad de MálagaSpain

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