P2AMF: Predictive, Probabilistic Architecture Modeling Framework

  • Pontus Johnson
  • Johan Ullberg
  • Markus Buschle
  • Ulrik Franke
  • Khurram Shahzad
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 144)


In the design phase of business and software system development, it is desirable to predict the properties of the system-to-be. Existing prediction systems do, however, not allow the modeler to express uncertainty with respect to the design of the considered system. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P2AMF), capable of advanced and probabilistically sound reasoning about architecture models given in the form of UML class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P2AMF adds a probabilistic inference mechanism. The paper introduces P2AMF, describes its use for system property prediction and assessment, and proposes an algorithm for probabilistic inference.


probabilistic inference system properties prediction Object Constraint Language UML class diagram object diagram 


  1. 1.
    Ullberg, J., Johnson, P., Buschle, M.: A language for interoperability modeling and prediction. Computers in Industry (2012)Google Scholar
  2. 2.
    Chen, D., Doumeingts, G., Vernadat, F.: Architectures for enterprise integration and interoperability: Past, present and future. Computers in Industry 59(7), 647–659 (2008)CrossRefGoogle Scholar
  3. 3.
    Object Management Group: Object Constraint Language. Version 2.3 (2010)Google Scholar
  4. 4.
    Object Management Group: UML Profile for MARTE: Modeling and Analysis of Real-Time Embedded Systems. Version 1.0 (2009)Google Scholar
  5. 5.
    Lodderstedt, T., Basin, D., Doser, J.: SecureUML: A UML-Based Modeling Language for Model-Driven Security. In: Jézéquel, J.-M., Hussmann, H., Cook, S. (eds.) UML 2002. LNCS, vol. 2460, pp. 426–441. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Hansson, H., Jonsson, B.: A Logic for Reasoning about Time and Reliability. Formal Aspects of Computing 6(5), 512–535 (1994)CrossRefGoogle Scholar
  7. 7.
    Ritchey, R., Ammann, P.: Using model checking to analyze network vulnerabilities. In: Proceedings of the 2000 IEEE Symposium on Security and Privacy, S & P 2000, pp. 156–165. IEEE (2000)Google Scholar
  8. 8.
    Object Management Group: OMG Unified Modeling Language (OMG UML), Superstructure. Version 2.4 (2011)Google Scholar
  9. 9.
    Ullberg, J., Franke, U., Buschle, M., Johnson, P.: A tool for interoperability analysis of enterprise architecture models using Pi-OCL. In: Enterprise Interoperability IV, pp. 81–90 (2010)Google Scholar
  10. 10.
    Spivey, J.M.: The Z notation: a reference manual. Prentice Hall International (UK) Ltd. (1992)Google Scholar
  11. 11.
    Lyu, M.R.: Handbook of Software Reliability Engineering. Mcgraw-Hill (1996)Google Scholar
  12. 12.
    Mason-Jones, R., Towill, D.R.: Total cycle time compression and the agile supply chain. International Journal of Production Economics 62(1-2) (1999)Google Scholar
  13. 13.
    Aier, S., Buckl, S., Franke, U., Gleichauf, B., Johnson, P., Närman, P., Schweda, C.M., Ullberg, J.: A survival analysis of application life spans based on enterprise architecture models. In: Proc. 3rd International Workshop on Enterprise Modelling and Information Systems Architectures, EMISA 2009. Lecture Notes in Informatics, pp. 141–154 (2009)Google Scholar
  14. 14.
    Franke, U., Johnson, P., König, J.: An architecture framework for enterprise IT service availability analysis. Software & Systems Modeling (2013)Google Scholar
  15. 15.
    Gustafsson, P., Höök, D., Franke, U., Johnson, P.: Modeling the IT impact on organizational structure. In: Proc. 13th IEEE International EDOC Conference, EDOC 2009 (2009)Google Scholar
  16. 16.
    Närman, P., Buschle, M., Ekstedt, M.: An enterprise architecture framework for multi-attribute information systems analysis. Software & Systems Modeling (2013)Google Scholar
  17. 17.
    Walsh, B.: Markov Chain Monte Carlo and Gibbs Sampling (2004)Google Scholar
  18. 18.
    Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D.L., Kolobov, A.: Blog: probabilistic models with unknown objects. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, IJCAI 2005, pp. 1352–1359. Morgan Kaufmann Publishers Inc. (2005)Google Scholar
  19. 19.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1300–1307. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  20. 20.
    Melton, J., Simon, A.: Understanding the new SQL: a complete guide. Morgan Kaufmann Publishers (1993)Google Scholar
  21. 21.
    Grassi, V., Mirandola, R., Randazzo, E., Sabetta, A.: KLAPER: An Intermediate Language for Model-Driven Predictive Analysis of Performance and Reliability. In: Rausch, A., Reussner, R., Mirandola, R., Plášil, F. (eds.) The Common Component Modeling Example. LNCS, vol. 5153, pp. 327–356. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Becker, S., Koziolek, H., Reussner, R.: The palladio component model for model-driven performance prediction. Journal of Systems and Software 82(1), 3–22 (2009)CrossRefGoogle Scholar
  23. 23.
    Ferrer, A.J.: Optimis: A holistic approach to cloud service provisioning. Future Generation Computer Systems 28(1), 66–77 (2012)CrossRefGoogle Scholar
  24. 24.
    Bass, L., Clements, P., Kazman, R.: Software Architecture in Practice, 2nd edn. Addison-Wesley Longman Publishing Co., Inc. (2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Pontus Johnson
    • 1
  • Johan Ullberg
    • 1
  • Markus Buschle
    • 1
  • Ulrik Franke
    • 1
    • 2
  • Khurram Shahzad
    • 1
  1. 1.Industrial Information and Control SystemsKTH Royal Institute of TechnologyStockholmSweden
  2. 2.FOI - Swedish Defence Research AgencyStockholmSweden

Personalised recommendations