Performance and dependability modeling with Möbius
ACM SIGMETRICS Performance Evaluation Review
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Recovering model invariants from simulation traces with Petri net analysis techniques
Winter Simulation Conference
Formal analysis of safety-critical system simulations
Proceedings of the 2nd International Conference on Application and Theory of Automation in Command and Control Systems
Modeling and wafer defect analysis in semiconductor automated material handling systems
Proceedings of the Winter Simulation Conference
Verification and testing of biological models
Proceedings of the Winter Simulation Conference
Conservation of Mass Analysis for Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Development for granular computing-based multi-agent system for data fusion process
International Journal of Computer Applications in Technology
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In this paper, we describe a novel technique that helps a modeler gain insight into the dynamic behavior of a complex stochastic discrete event simulation model based on trace analysis. We propose algorithms to distinguish progressive from repetitive behavior in a trace and to extract a minimal progressive fragment of a trace. The implied combinatorial optimization problem for trace reduction is solved in linear time with dynamic programming. We present and compare several approximate and one exact solution method. Information on the reduction operation as well as the reduced trace itself helps a modeler to recognize the presence of certain errors and to identify their cause. We track down a subtle modeling error in a dependability model of a multi-class server system to illustrate the effectiveness of our approach in revealing the cause of an observed effect. The proposed technique has been implemented and integrated in Traviando, a trace analyzer to debug stochastic simulation models.