ENHANCING UNDERSTANDING OF MODELS THROUGH ANALYSIS

Kara A. Olson, C. Michael Overstreet

2011

Abstract

Simulation is used increasingly throughout research and development for many purposes. While in many cases the model output is of primary interest, often it is the insight gained through the simulation process into the behavior of the simulated system that is the primary benefit. This insight can come from the actions of building and validating the model as well as observing its behavior through animations and execution traces or statistical analysis of simulation output. However, much that could be of interest may not be easily discernible through these traditional approaches, particularly as models become increasingly complex. The authors suggest several possibilities of how to obtain such insights. These analyses have other obvious uses including aid in debugging, verification and documentation. The authors, however, are primarily interested in how these analysis techniques can be used to help modelers gain additional insights into the models they are using or constructing. The discussed techniques are used with significant benefit within computer science and software engineering; the authors believe these techniques can also serve simulation well. The authors’ experience with these techniques thus far has involved discrete event simulations; their potential benefit with other model representations and implementation approaches has not yet been explored.

References

  1. Anderson, P., Reps, T. W., Teitelbaum, T., and Zarnis, M. (2003). Tool support for fine-grained software inspection. IEEE Software, 20(4):42-50.
  2. Baecker, R. (1988). Enhancing program readability and comprehension with tools for program visualization. In Proceedings of the 10th International Conference on Software Engineering, pages 356-366.
  3. Glenberg, A. M. and Langston, W. E. (1992). Comprehension of illustrated text: Pictures help to build mental models. J. Mem. Lang., 31(2):129-151.
  4. Hwang, M. H. and Zeigler, B. P. (2006). A modular verification framework based on finite & deterministic DEVS. In Proceedings of the 2006 DEVS Integrative M&S Symposium, pages 57-65.
  5. Jerding, D. F. and Stasko, J. T. (1998). The information mural: A technique for displaying and navigating large information spaces. IEEE Trans. Vis. Comput. Graph., 4(3):257-271.
  6. Murata, T. (1989). Petri nets: Properties, analysis and applications. Proc. IEEE, 77(4):541-580.
  7. Nance, R. E., Overstreet, C. M., and Page, E. H. (1999). Redundancy in model specifications for discrete event simulation. ACM Trans. Model. Comput. Simul., 9(3):254-281.
  8. Nassi, I. and Schneiderman, B. (1973). Flowcharting techniques for structured programming. ACM SIGPLAN Notices, 8(8):12-26.
  9. Overstreet, C. M. (1982). Model Specification and Analysis for Discrete Event Simulation. PhD thesis, Virginia Polytechnic Institute and State University, Blacksburg, VA.
  10. Overstreet, C. M. and Levinstein, I. B. (2004). Enhancing understanding of model behavior through collaborative interactions. Operational Research Society (UK) Simulation Study Group 2nd Two Day Workshop.
  11. Paul, R. J., Eldabi, T., Kuljis, J., and Taylor, S. J. E. (2005). Is problem solving, or simulation model solving, mission critical? In Kuhl, M. E., Steiger, N. M., Armstrong, F. B., and Joines, J. A., editors, Proceedings of the 2005 Winter Simulation Conference, pages 547- 554.
  12. Paul, R. J. and Kuljis, J. (2010). Problem solving, model solving, or what? In Johansson, B., Jain, S., Hugan, J. R. M.-T. J. C., and Yücesan, E., editors, Proceedings of the 2010 Winter Simulation Conference, pages 353- 358.
  13. Schruben, L. (1983). Simulation modeling with event graphs. Comm. ACM, 26(11):957-963.
  14. Weinberg, G. M. (1971). The Psychology of Computer Programming. Computer Science Series. Van Nostrand Reinhold Company, New York, NY.
  15. Weiser, M. (1984). Program slicing. IEEE Trans. Softw. Eng., SE-10(4):352-357.
  16. Wikipedia (2011). Wikipedia. Axiomatic system, http://en.wikipedia.org/wiki/Axiomatic system.
  17. Zeigler, B. P. (1990). Object-Oriented Simulation with Hierarchical, Modular Models. Academic Press, Boston, MA.
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Paper Citation


in Harvard Style

A. Olson K. and Overstreet C. (2011). ENHANCING UNDERSTANDING OF MODELS THROUGH ANALYSIS . In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-8425-78-2, pages 321-326. DOI: 10.5220/0003597603210326


in Bibtex Style

@conference{simultech11,
author={Kara A. Olson and C. Michael Overstreet},
title={ENHANCING UNDERSTANDING OF MODELS THROUGH ANALYSIS},
booktitle={Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2011},
pages={321-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003597603210326},
isbn={978-989-8425-78-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - ENHANCING UNDERSTANDING OF MODELS THROUGH ANALYSIS
SN - 978-989-8425-78-2
AU - A. Olson K.
AU - Overstreet C.
PY - 2011
SP - 321
EP - 326
DO - 10.5220/0003597603210326