Dynamic Reactive Modules

  • Jasmin Fisher
  • Thomas A. Henzinger
  • Dejan Nickovic
  • Nir Piterman
  • Anmol V. Singh
  • Moshe Y. Vardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6901)


State-transition systems communicating by shared variables have been the underlying model of choice for applications of model checking. Such formalisms, however, have difficulty with modeling process creation or death and communication reconfigurability. Here, we introduce “dynamic reactive modules” (DRM), a state-transition modeling formalism that supports dynamic reconfiguration and creation/death of processes. The resulting formalism supports two types of variables, data variables and reference variables. Reference variables enable changing the connectivity between processes and referring to instances of processes. We show how this new formalism supports parallel composition and refinement through trace containment. DRM provide a natural language for modeling (and ultimately reasoning about) biological systems and multiple threads communicating through shared variables.


Model Check Shared Variable Transition Relation Parallel Composition Dynamic Discrete System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alur, R., Grosu, R.: Dynamic Reactive Modules. Tech. Rep. 2004/6, Stony Brook (2004)Google Scholar
  2. 2.
    Alur, R., Henzinger, T.A.: Reactive modules. FMSD 15(1), 7–48 (1999)Google Scholar
  3. 3.
    Attie, P.C., Lynch, N.A.: Dynamic input/output automata, a formal model for dynamic systems. In: PODC, pp. 314–316 (2001)Google Scholar
  4. 4.
    Clarke, E.M., Grumberg, O., Peled, D.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    Colaço, J.-L., Girault, A., Hamon, G., Pouzet, M.: Towards a higher-order synchronous data-flow language. In: EmSoft, pp. 230–239. ACM, New York (2004)CrossRefGoogle Scholar
  6. 6.
    Damm, W., Josko, B., Pnueli, A., Votintseva, A.: A discrete-time UML semantics for concurrency and communication in safety-critical applications. SCP 55(1-3), 81–115 (2005)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Efroni, S., Harel, D., Cohen, I.R.: Toward rigorous comprehension of biological complexity: Modeling, execution, and visualization of thymic T-cell maturation. Genome. Res. 13(11), 2485–2497 (2003)Google Scholar
  8. 8.
    Efroni, S., Harel, D., Cohen, I.R.: Emergent dynamics of thymocyte development and lineage determination. PLoS Comput. Biol. 3(1), e13 (2007)Google Scholar
  9. 9.
    Fisher, J., Piterman, N., Hajnal, A., Henzinger, T.A.: Predictive modeling of signaling crosstalk during C. elegans vulval development. PLoS Comput. Biol. 3(5), e92 (2007)CrossRefGoogle Scholar
  10. 10.
    Fisher, J., Piterman, N., Hubbard, E.J.A., Stern, M.J., Harel, D.: Computational insights into Caenorhabditis elegans vulval development. Proc. Natl. Acad. Sci. 102(6), 1951–1956 (2005)CrossRefGoogle Scholar
  11. 11.
    Harel, D.: Statecharts: A visual formalism for complex systems. Sci. Comput. Program. 8(3), 231–274 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Harel, D., Kugler, H.: The rhapsody semantics of statecharts (or, on the executable core of the UML). In: Ehrig, H., Damm, W., Desel, J., Große-Rhode, M., Reif, W., Schnieder, E., Westkämper, E. (eds.) INT 2004. LNCS, vol. 3147, pp. 325–354. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Harel, D., Naamad, A.: The STATEMATE semantics of statecharts. ACM Trans. Softw. Eng. Methodol. 5(4), 293–333 (1996)CrossRefGoogle Scholar
  14. 14.
    Kesten, Y., Pnueli, A.: Verification by augmented finitary abstraction. Inf. Comput. 163(1), 203–243 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Kleene, S.C.: Introduction to Mathematics. North-Holland, Amsterdam (1987)Google Scholar
  16. 16.
    Lynch, N., Tuttle, M.: An introduction to input/output automata. In: Distributed Systems Engineering (1988)Google Scholar
  17. 17.
    Mandel, L., Pouzet, M.: ReactiveML: a reactive extension to ML. In: PPDP, pp. 82–93. ACM, New York (2005)Google Scholar
  18. 18.
    Milner, R.: A Calculus of Communication Systems. LNCS, vol. 92. Springer, Heidelberg (1980)CrossRefzbMATHGoogle Scholar
  19. 19.
    Milner, R.: The polyadic pi-calculus (abstract). In: Cleaveland, W.R. (ed.) CONCUR 1992. LNCS, vol. 630. Springer, Heidelberg (1992)Google Scholar
  20. 20.
    Milner, R., Parrow, J., Walker, D.: A calculus of mobile processes, i & ii. Inf. Comput. 100(1), 1–77 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Plotkin, G.D.: A structural approach to operational semantics. Technical Report DAIMI FN-19, Computer Science Department, Aarhus University (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jasmin Fisher
    • 1
  • Thomas A. Henzinger
    • 2
  • Dejan Nickovic
    • 2
  • Nir Piterman
    • 3
  • Anmol V. Singh
    • 2
  • Moshe Y. Vardi
    • 4
  1. 1.Microsoft ResearchCambridgeUK
  2. 2.IST AustriaKlosterneuburgAustria
  3. 3.University of LeicesterUK
  4. 4.Rice UniversityHoustonUSA

Personalised recommendations