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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Simulation Goals and Metrics Identification

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DOI: http://dx.doi.org/10.15439/2016F506

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 14911494 ()

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Abstract. Agent-Based Modeling and Simulation (ABMS) is a very useful means for producing high quality models during simulation studies. When ABMS is part of a methodological ap- proach it becomes important to have a method for identifying the objectives of the simulation study in a disciplined fashion. In this work we propose a set of guidelines for properly capturing and representing the goals of the simulations and the metrics, allowing and evaluating the achievement of a simulation objective. We take inspiration from the goal-question-metric approach and with the aid of a specific problem formalization we are able to derive the right questions for relating simulation goals and metrics.


  1. I. Carson and S. John, “Introduction to modeling and simulation,” in Proceedings of the 36th conference on Winter simulation. Winter Simulation Conference, 2004, pp. 9–16.
  2. O. Balci, “Guidelines for successful simluation studies (tutorial session),” in Proceedings of the 22nd conference on Winter simulation. IEEE Press, 1990, pp. 25–32.
  3. O. Balci, “Validation, verification, and testing techniques throughout the life cycle of a simulation study,” Annals of operations research, vol. 53, no. 1, pp. 121–173, 1994.
  4. F. Klügl, “Multiagent simulation model design strategies.” in MALLOW, 2009.
  5. A. Garro and W. Russo, “easyabms: A domain-expert oriented methodology for agent-based modeling and simulation,” Simulation Modelling Practice and Theory, vol. 18, no. 10, pp. 1453–1467, 2010.
  6. F. Klügl, “Engineering agent-based simulation models?” in Agent-Oriented Software Engineering XIII. Springer, 2012, pp. 179–196.
  7. R. Van Solingen, V. Basili, G. Caldiera, and H. D. Rombach, “Goal question metric (gqm) approach,” Encyclopedia of software engineering, 2002.
  8. M. Cossentino, C. Lodato, P. Ribino, and V. Seidita, “A heuristic for problem formalization in agent based simulation studies,” in Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on. IEEE, 2015, pp. 1733–1743.
  9. V. R. Basili and D. M. Weiss, “A methodology for collecting valid software engineering data,” IEEE Transactions on Software Engineering, vol. 6, no. SE-10, pp. 728–738, 1984.
  10. V. R. Basili, S. Green, O. Laitenberger, F. Lanubile, F. Shull, S. Sørumgård, and M. V. Zelkowitz, “The empirical investigation of perspective-based reading,” Empirical Software Engineering, vol. 1, no. 2, pp. 133–164, 1996.
  11. N. Fenton and J. Bieman, Software metrics: a rigorous and practical approach. CRC Press, 2014.
  12. A. M. Law, “How to build valid and credible simulation models,” in Proceedings of the 40th Conference on Winter Simulation. Winter Simulation Conference, 2008, pp. 39–47.
  13. P. Ribino, V. Seidita, C. Lodato, S. Lopes, and M. Cossentino, “Common and domain-specific metamodel elements for problem description in simulation problems,” in Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on. IEEE, 2014, pp. 1467–1476.