Modular DSLs for Flexible Analysis: An e-Motions Reimplementation of Palladio

  • Antonio Moreno-Delgado
  • Francisco Durán
  • Steffen Zschaler
  • Javier Troya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8569)


We address some of the limitations for extending and validating MDE-based implementations of NFP analysis tools by presenting a modular, model-based partial reimplementation of one well-known analysis framework, namely the Palladio Architecture Simulator. We specify the key DSLs from Palladio in the e-Motions system, describing the basic simulation semantics as a set of graph transformation rules. Different properties to be analysed are then encoded as separate, parametrised DSLs, independent of the definition of Palladio. These can then be composed with the base Palladio DSL to generate specific simulation environments. Models created in the Palladio IDE can be fed directly into this simulation environment for analysis. We demonstrate two main benefits of our approach: 1) The semantics of the simulation and the non-functional properties to be analysed are made explicit in the respective DSL specifications, and 2) because of the compositional definition, we can add definitions of new non-functional properties and their analyses.


Abstract Syntax Concrete Syntax Eclipse Modeling Framework Graph Transformation Rule Observer Object 
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  1. 1.
    Balsamo, S., DiMarco, A., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: A survey. IEEE Transactions on Software Engineering 30(5), 295–310 (2004)CrossRefGoogle Scholar
  2. 2.
    Becker, S., Grunske, L., Mirandola, R., Overhage, S.: Performance prediction of component-based systems: A survey from an engineering perspective. In: Reussner, R., Stafford, J.A., Ren, X.-M. (eds.) Architecting Systems. LNCS, vol. 3938, pp. 169–192. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Becker, S., Koziolek, H., Reussner, R.: Model-based performance prediction with the Palladio component model. In: Proc. 6th Int’l Workshop on Software and Performance (WOSP 2007). ACM (2007)Google Scholar
  4. 4.
    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
  5. 5.
    Durán, F., Orejas, F., Zschaler, S.: Behaviour protection in modular rule-based system specifications. In: Martí-Oliet, N., Palomino, M. (eds.) WADT 2012. LNCS, vol. 7841, pp. 24–49. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Durán, F., Zschaler, S., Troya, J.: On the reusable specification of non-functional properties in DSLs. In: Czarnecki, K., Hedin, G. (eds.) SLE 2012. LNCS, vol. 7745, pp. 332–351. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Fritzsche, M., Johannes, J., Zschaler, S., Zherebtsov, A., Terekhov, A.: Application of tracing techniques in model-driven performance engineering. In: 4th ECMDA Traceability Workshop (2008)Google Scholar
  8. 8.
    Grassi, V., Mirandola, R.: A model-driven approach to predictive non functional analysis of component-based systems. In: Proc. Workshop on Models for Non-Functional Aspects of Component-Based Software (2004)Google Scholar
  9. 9.
    Happe, J., Koziolek, H., Reussner, R.: Facilitating performance predictions using software components. IEEE Software 28(3), 27–33 (2011)CrossRefGoogle Scholar
  10. 10.
    Moreno-Delgado, A., Troya, J., Durán, F., Vallecillo, A.: On the Modular Specification of NFPs: A Case Study. In: Proc. of XVIII JISBD, pp. 302–316 (2013)Google Scholar
  11. 11.
    Rivera, J.E., Durán, F., Vallecillo, A.: A graphical approach for modeling time-dependent behavior of DSLs. In: Proc. of VL/HCC 2009. IEEE (2009)Google Scholar
  12. 12.
    Röttger, S., Zschaler, S.: Tool support for refinement of non-functional specifications. Software and Systems Modeling Journal (SoSyM) 6(2), 185–204 (2007)CrossRefGoogle Scholar
  13. 13.
    Smith, C.U., Williams, L.G.: Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Object-Technology Series. Addison-Wesley (2002)Google Scholar
  14. 14.
    Spinner, S., Kounev, S., Meier, P.: Stochastic modeling and analysis using QPME: Queueing petri net modeling environment v2.0. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 388–397. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Troya, J., Rivera, J.E., Vallecillo, A.: Simulating Domain Specific Visual Models by Observation. In: Proc. of the 2010 Spring Simulation Multiconference, SpringSim 2010, pp. 128:1–128:8. ACM, New York (2010)Google Scholar
  16. 16.
    Troya, J., Vallecillo, A.: A domain specific visual language for modeling power-aware reliability in wireless sensor networks. In: Proc. of NFPinDSML 2012, pp. 3:1–3:6. ACM (2012)Google Scholar
  17. 17.
    Troya, J., Vallecillo, A., Durán, F., Zschaler, S.: Model-driven performance analysis of rule-based domain specific visual models. Information and Software Technology 55(1), 88–110 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Antonio Moreno-Delgado
    • 1
  • Francisco Durán
    • 1
  • Steffen Zschaler
    • 2
  • Javier Troya
    • 3
  1. 1.University of MálagaSpain
  2. 2.King’s College LondonUK
  3. 3.Vienna University of TechnologyAustria

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