Formalizing Collaboration Goal Sequences for Service Choreography

  • Humberto Nicolás Castejón
  • Rolv Bræk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4229)


Methods for service specification should be simple and intuitive. At the same time they should be precise and allow early validation and detection of inconsistencies. UML 2.0 collaborations enable a systematic and structured way to provide overview of distributed services, and decompose cross-cutting service behaviour into features and interfaces by means of collaboration-uses. To fully take advantage of the possibilities thus opened, a way to compose (i.e. choreograph) the joint collaboration behaviour is needed. So-called collaboration goal sequences have been introduced for this purpose. They describe the behavioural composition of collaboration-uses (modeling interface behaviour and features) within a composite collaboration. In this paper we propose a formal semantics for collaboration goal sequences by means of hierarchical coloured Petri-nets (HCPNs). We then show how tools available for HCPNs can be used to automatically analyse goal sequences in order to detect implied scenarios.


Formal Semantic Outgoing Edge Decision Node Active Collaboration Substitution Transition 
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.


  1. 1.
    Alur, R., Etessami, K., Yannakakis, M.: Inference of message sequence charts. In: 22nd Intl. Conf. on Software Engineering (ICSE 2000), pp. 304–313 (2000)Google Scholar
  2. 2.
    Castejón, H.N., Bræk, R.: A collaboration-based approach to service specification and detection of implied scenarios. In: ICSE’s 5th Intl. Workshop on Scenarios and State Machines: models, algorithms and tools (SCESM 2006). ACM Press, New York (2006)Google Scholar
  3. 3.
    CPN Group: CPN Tools Manual. Technical report, Univ. of Aarhus, Denmark (2005), Available at:
  4. 4.
    Jensen, K.: Coloured Petri Nets. Basic Concepts, Analysis Methods and Practical Use, vol. 1. Springer, Heidelberg (1997)CrossRefMATHGoogle Scholar
  5. 5.
    Krüger, I.H., Gupta, D., Mathew, R., Moorthy, P., Phillips, W., Rittmann, S., Ahluwalia, J.: Towards a process and tool-chain for service-oriented automotive software engineering. In: ICSE 2004 Workshop on Software Engineering for Automotive Systems (SEAS) (2004)Google Scholar
  6. 6.
    Muccini, H.: Detecting implied scenarios analyzing non-local branching choices. In: Pezzé, M. (ed.) FASE 2003. LNCS, vol. 2621, pp. 372–386. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Object Management Group: UML 2.0 Superstructure Specification (2005)Google Scholar
  8. 8.
    Rößler, F., Geppert, B., Gotzhein, R.: Collaboration-based design of SDL systems. In: Reed, R., Reed, J. (eds.) SDL 2001. LNCS, vol. 2078, pp. 72–89. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Sanders, R.T., Bræk, R.: Modeling peer-to-peer service goals in UML. In: 2nd IEEE Intl. Conf. on Software Engineering and Formal Methods (SEFM 2004) (2004)Google Scholar
  10. 10.
    Sanders, R.T., Bræk, R., von Bochmann, G., Amyot, D.: Service discovery and component reuse with semantic interfaces. In: Prinz, A., Reed, R., Reed, J. (eds.) SDL 2005. LNCS, vol. 3530, pp. 85–102. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Sanders, R.T., Castejón, H.N., Kraemer, F.A., Bræk, R.: Using UML 2.0 collaborations for compositional service specification. In: Briand, L.C., Williams, C. (eds.) MoDELS 2005. LNCS, vol. 3713, pp. 460–475. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Uchitel, S., Kramer, J., Magee, J.: Incremental elaboration of scenario-based specifications and behavior models using implied scenarios. ACM TOSEM 13 (2004)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Humberto Nicolás Castejón
    • 1
  • Rolv Bræk
    • 1
  1. 1.Department of TelematicsNTNUTrondheimNorway

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