ITM Web of Conferences
Volume 3, 2014ACTIMS 2014 – Activity-Based Modeling & Simulation 2014
|Number of page(s)||8|
|Section||Modeling and Simulation Software|
|Published online||02 December 2014|
Using Activity Metrics for DEVS Simulation Profiling
1 UMR CNRS 6070 I3S Laboratory, Team Bio-info, Batiment Algorithme, Euclide-B - B.P. 121, 2000 Route des Lucioles, 06903 Sophia Antipolis, France
2 SPE UMR CNRS 6134 Laboratory, University of Corsica, Campus Grimaldi, 2050, Corte, France
a A. Muzy is with the I3S Laboratory (UMR CNRS 6070), Team Bio-info, Batiment Algorithme, Euclide-B - B.P. 121, 2000 Route des Lucioles, 06903 Sophia Antipolis France, e-mail: Alexandre.Muzy@cnrs.fr
b L. Capocchi is with the SPE Laboratory (UMR CNRS 6134), University of Corsica, Quartier Grimaldi, 20250, Corte, France, e-mail: firstname.lastname@example.org
c J.F. Santucci is with the SPE Laboratory (UMR CNRS 6134), University of Corsica, Quartier Grimaldi, 20250, Corte, France, e-mail: email@example.com
Activity metrics can be used to profile DEVS models before and during the simulation. It is critical to get good activity metrics of models before and during their simulation. Having a means to compute a-priori activity of components (analytic activity) may be worth when simulating a model (or parts of it) for the first time. After, during the simulation, analytic activity can be corrected using dynamic one. In this paper, we introduce McCabe cyclomatic complexity metric (MCA) to compute analytic activity. Both static and simulation activity metrics have been implemented through a plug-in of the DEVSimPy (DEVS Simulator in Python language) environment and applied to DEVS models.
© Owned by the authors, published by EDP Sciences, 2014
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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