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ABSTRACT
Current complexity metrics based upon graphical analysis or static program characteristics are not well suited for discrete event simulation model representations, owing to their inherent dynamics. This paper describes a complexity metric suitable for model representations. A study of existing metrics provides a basis for the development of the desired metric, and a set of characteristics for a simulation model complexity metric is defined. A metric is described based on the two types of complexity that are apparent in model representations. Experimental data are presented to verify that this metric possesses the desired characteristics. Based on evaluation of this data and the desired characteristics, this metric appears to offer an improvement over existing metrics.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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1
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Chapin, N. (1979). A Measure of Program Complexity. Proceedinqs 1979 AFIPS National Computer Conference, 995-1002.
|
| |
2
|
Curtis, B. (1980~. Measurement and Experimentation in Software Engineering. Proceedinqs of the IEEE 68, 1144-1157.
|
| |
3
|
Davis, J.S. (1984). Chunks: A Basis for Complexity Measurement. Information Processinq and Manaqement 20, 119-127.
|
| |
4
|
|
 |
5
|
|
| |
6
|
|
| |
7
|
Henry, S. and D. Kafura (1981). Software Metrics Based on Information Flow. IEEE Transactions on Software EnQineerinq SE-7, 510-518.
|
| |
8
|
McCabe, T.J. (1976). A Complexity Measure," IEEE Transactions on Software Enqineerinq SE-2, 308-320.
|
| |
9
|
Mills, H.S. (1973). The Complexity of Programs. Pro qram Test Methods, W.C. Hetzel, ed., Prentice-Hall, Inc. Englewood Cliffs, N J, 225-238.
|
 |
10
|
|
 |
11
|
|
| |
12
|
Nance, R.E. ( 1971). On Time Flow Mechanisms for Discrete System Simulation. ManaqementScience 18, 59-73_
|
| |
13
|
Overstreet, C. M. and R. E. Nance (1986). World View Based Discrete Event Model Simplification. Modellinq and Simulation Methodoloqv in the Artificial Intelliqen.ce Era, M. S. Elzas, T.I. Oren and B. P. Zeigler, ed., Elsevier North-Holland, Inc., New York, NY.
|
| |
14
|
Overstreet, C.M. and R.E. Nance (1984). Graph-based Diagnosis of Discrete Event Model Specifications. Technical Report CS83028-R, Department of Computer Science, Virginia Tech.
|
| |
15
|
|
| |
16
|
Wallace, J.C. (1985). The Control and Transformation Metric: A Basis for Measuring Model Complexity. Technical Report TR-85-15, Systems Research Center, Virginia Tech, BLackburg, Virginia.
|
| |
17
|
|
| |
18
|
Woodward, M.R., M.A. Hennell and D. Hedley (1979). A Measure of Control Flow Complexity in Program Text. IEEE Transactions on Software E_nclineerinq, SE-5, 45- 50.
|
| |
19
|
|
| |
20
|
Zolnowski, J.M. and D.B. Simmons (1977). Measuring Program Complexity. Proceedinqs of the 1977 Fall COMPCON, !.EEE, 336-340.
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CITED BY 6
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Osman Balci , Anders I. Bertelrud , Chuck M. Esterbrook , Richard E. Nance, The Visual Simulation Environment technology transfer, Proceedings of the 29th conference on Winter simulation, p.1323-1329, December 07-10, 1997, Atlanta, Georgia, United States
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S. Cem Karacal , Terrence G. Beaumariage , Zeynep A. Karacal, Comparison of simulation environments through analytic hierarchy process, Proceedings of the 28th conference on Winter simulation, p.740-747, December 08-11, 1996, Coronado, California, United States
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