Developing a Simulation Model Using a SPEM-Based Process Model and Analytical Models

  • Seunghun Park
  • Hyeonjeong Kim
  • Dongwon Kang
  • Doo-Hwan Bae
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 10)


It is hard to adopt a simulation technology because of the difficulty in developing a simulation model. In order to resolve the difficulty, we consider the following issues: reducing the cost to develop a simulation model, reducing the simulation model complexity, and resolving the lack of historical data. We propose an approach to deriving a simulation model from a descriptive process model and widely adopted analytical models. We provide a method to develop simulation models and a tool environment to support the method. We applied our approach in developing the simulation model for a government project. Our approach resolves the issues by the transformation algorithms, the hierarchical and modularized modeling properties of UML and (Discrete Event System Specification) DEVS, and widely adopted analytical models.


Simulation modeling Hybrid simulation model Software process modeling SPEM Analytical models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Raffo, D., Spehar, G., Nayak, U.: Generalized Simulation Models: What, Why and How? In: ProSim 2003, Oregon (2003)Google Scholar
  2. 2.
    Pfahl, D., Ruhe, G.: IMMoS: A Methodology for Integrated Measurement, Modelling and Simulation. Software Process Improvement and Practice 7(3-4), 189–210 (2002)CrossRefGoogle Scholar
  3. 3.
    Boehm, B.: Software Cost Estimation with COCOMO-II. Prentice-Hall, Englewoord Cliffs (2006)Google Scholar
  4. 4.
    Park, S., Choi, K., Yoon, K., Bae, D.: Deriving Software Process Simulation Model from SPEM-based Software Process Model. In: APSEC 2007, Nagoya, Japan, pp. 382–389 (2007)Google Scholar
  5. 5.
    Software Process Engineering Metamodel Specification, Version 1.1, OMG Document formal/05-01-06 (2005)Google Scholar
  6. 6.
    Choi, K., Bae, D., Kim, T.: An approach to a hybrid software process simulation using DEVS formalism. Software Process Improvement and Practice 11(4), 373–383 (2006)CrossRefGoogle Scholar
  7. 7.
    Zeigler, B., Pracehofer, H., Kim, T.: Theory of Modeling and Simulation, 2nd edn. Academic Press, New York (2000)Google Scholar
  8. 8.
    Choi, K.: Hybrid Software Process Simulation Modeling for Analyzing Software-Intensive System Acquisition. Ph.D Dissertation, KAIST (2007)Google Scholar
  9. 9.
    Kim, T.: DEVSimHLA v2.2.0 Developer’s Manual, KAIST (2004)Google Scholar
  10. 10.
    Abdel-Hamid, T., Madnick, S.: Software Project Dynamics: An Integrated Approach. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
  11. 11.
    Donzelli, P.: A Decision Support System for Software Project Management. IEEE Software 23(4), 67–75 (2006)CrossRefGoogle Scholar
  12. 12.
    Madachy, R.: System dynamics modeling of an inspection-based process. In: Proceeding of the 18th ICSE, pp. 376–386 (1996)Google Scholar
  13. 13.
    Lara, J., Taentzer, G.: Automated Model Transformation and its Validation Using AToM3 and AGG. In: Blackwell, A.F., Marriott, K., Shimojima, A. (eds.) Diagrams 2004. LNCS (LNAI), vol. 2980, pp. 182–198. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Smith, J.: The estimation of effort based on use cases. Rational Software, white paper (1999)Google Scholar
  15. 15.
    Mohagheghi, P., Anda, B., Conradi, R.: Effort Estimation of Use Cases for Incremental Large-Scale Software Development. In: ICSE 2005, St Louis, USA, pp. 303–311 (2005)Google Scholar
  16. 16.
  17. 17.
    Raffo, D., Nayak, U., Wakeland, W.: Implementing Generalized Process Simulation Models. In: ProSim 2005, St. Louis (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Seunghun Park
    • 1
  • Hyeonjeong Kim
    • 1
  • Dongwon Kang
    • 1
  • Doo-Hwan Bae
    • 1
  1. 1.Division of Computer Science, Department of EECSKAISTKorea

Personalised recommendations