A Generalized Formal Framework for Partial Modeling

  • Rick SalayEmail author
  • Marsha Chechik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9033)


Uncertainty is pervasive within software engineering, negatively affecting software quality as well as development time. In previous work, we have developed a language-independent partial modeling technique called MAVO that allows a software modeler to explicitly express and reason about model uncertainty. The cost of such a broadly applicable technique was to focus exclusively on the syntactic aspects of models. In addition, we have found that while MAVO expresses uncertainty at the model level, it is often more natural to do so for the entire submodels.

In this paper, we introduce a new language-independent formal framework for partial modeling called GMAVO that generalizes MAVO by providing the means for addressing model semantics and allowing uncertainty to be specified at the granularity of a submodel. We then show that GMAVO is sufficiently general to express Modal Transition Systems (MTSs) – an established “semantics-aware” partial behavioral modeling formalism.


Class Diagram Category Theory Partial Modeling Software Product Line Requirement Engineer 
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  1. 1.
    Diskin, Z.: Algebraic Models for Bidirectional Model Synchronization. In: Czarnecki, K., Ober, I., Bruel, J.-M., Uhl, A., Völter, M. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 21–36. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Egyed, A., Letier, E., Finkelstein, A.: Generating and Evaluating Choices for Fixing Inconsistencies in UML Design Models. In: Proc. of ASE 2008, pp. 99–108 (2008)Google Scholar
  3. 3.
    Ehrig, H., Ehrig, K., Prange, U., Taentzer, G.: Fundamentals of Algebraic Graph Transformation, vol. 373. Springer (2006)Google Scholar
  4. 4.
    Famelis, M., Chechik, M., Salay, R.: Partial Models: Towards Modeling and Reasoning with Uncertainty. In: Proc. of ICSE 2012, pp. 573–583 (2012)Google Scholar
  5. 5.
    Famelis, M., Santosa, S.: MAV-Vis: A Notation for Model Uncertainty. In: Proc. of MiSE 2013, pp. 7–12 (2013)Google Scholar
  6. 6.
    Fischbein, D., D’Ippolito, N., Brunet, G., Chechik, M., Uchitel, S.: Weak Alphabet Merging of Partial Behavior Models. ACM Trans. Softw. Eng. Methodol. 21(2) (2012)Google Scholar
  7. 7.
    Islam, S., Houmb, S.H.: Integrating Risk Management Activities into Requirements Engineering. In: Proc. of RCIS 2010, pp. 299–310 (2010)Google Scholar
  8. 8.
    Keller, R.: Formal Verification of Parallel Programs. Communications of the ACM 19(7), 371–384 (1976)CrossRefzbMATHGoogle Scholar
  9. 9.
    Larsen, K.G., Thomsen, B.: A Modal Process Logic. In: Proc. of LICS 1988 (1988)Google Scholar
  10. 10.
    Larsen, P.: The Expressive Power of Implicit Specifications. In: Leach Albert, J., Monien, B., Rodríguez-Artalejo, M. (eds.) ICALP 1991. LNCS, vol. 510, pp. 204–216. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  11. 11.
    Milner, R.: Communication and Concurrency. Prentice-Hall, New York (1989)zbMATHGoogle Scholar
  12. 12.
    OMG. Meta Object Facility (MOF) (2006)Google Scholar
  13. 13.
    Pohl, K., Böckle, G., Linden, F.V.D.: Software Product Line Engineering: Foundations, Principles, and Techniques. Springer-Verlag New York Inc. (2005)Google Scholar
  14. 14.
    Ramirez, A.J., Cheng, B.H.C., Bencomo, N., Sawyer, P.: Relaxing Claims: Coping with Uncertainty While Evaluating Assumptions at Run Time. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 53–69. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Sabetzadeh, M., Easterbrook, S.: View Merging in the Presence of Incompleteness and Inconsistency. J. Requirements Engineering 11(3), 174–193 (2006)CrossRefGoogle Scholar
  16. 16.
    Sabetzadeh, M., Nejati, S., Chechik, M., Easterbrook, S.: Reasoning about Consistency in Model Merging. In: Proc. of LWI 2010 (2010)Google Scholar
  17. 17.
    Salay, R., Chechik, M., Gorzny, J.: Towards a Methodology for Verifying Partial Model Refinements. In: Proc. of VOLT 2012 (April 2012)Google Scholar
  18. 18.
    Salay, R., Famelis, M., Chechik, M.: Language Independent Refinement Using Partial Modeling. In: de Lara, J., Zisman, A. (eds.) Fundamental Approaches to Software Engineering. LNCS, vol. 7212, pp. 224–239. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Salay, R., Gorzny, J., Chechik, M.: Change Propagation due to Uncertainty Change. In: Cortellessa, V., Varró, D. (eds.) FASE 2013. LNCS, vol. 7793, pp. 21–36. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Sobociński, P.: Relational Presheaves as Labelled Transition Systems. In: Pattinson, D., Schröder, L. (eds.) CMCS 2012. LNCS, vol. 7399, pp. 40–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    van Lamsweerde, A.: Requirements Engineering - From System Goals to UML Models to Software Specifications. Wiley (2009)Google Scholar
  22. 22.
    Ziv, H., Richardson, D., Klösch, R.: The Uncertainty Principle in Software Engineering (1996)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.University of TorontoTorontoCanada

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