| Several inherent constraints remain in the model development process, even though modern enhancements to simulation environments have provided tools for code generation, debugging, and tracing. To develop a simulation model, the simulation analyst still needs to have expertise in a number of different fields, e.g., probability, statistics, design of experiments, modeling, systems engineering, software engineering, and computer programming. Although several simulation packages implement syntactic-checks and semantic-consistency-checks, typically, the simulation analyst needs to possess output-analysis-knowledge specifically aimed at verifying and checking the simulation code.; Reverse engineering a graphical model, e.g., an event graph, from general purpose simulation code demonstrates an enhancement to the model development process. A reverse engineering step allows an analyst to check, both, the static and dynamic properties of the coded simulation model. Better diagnostic assistance is achieved when viewing a combination of static and dynamic properties of the simulation code. Now, the analyst is able to find logical/execution errors, e.g., errors related to resource deadlocks, before running simulation experiments. Since the graphical model is generated from the simulation code, the analyst also has a better framework for verifying the coded simulation model. Also, the reverse engineering step provides a structural model useful in converting between different simulation languages or systems. As well as verifying simulation code, the reverse engineering of event graphs helps in validating model designs to specifications.; Furthermore, it is sometimes desirable to know when two different discrete-event simulation models are, in some sense, interchangeable. It is difficult or impossible to assert that two simulations are behaviorally equivalent for all possible experiments. Therefore, defining a testable property related to behavioral equivalence which is associated with structural equivalence is critical to enhancing the process for analysis, verification, and validation of discrete-event simulation models. Having this testable property allows for determination when it is safe to substitute one model for another. It then becomes possible to develop algorithms for addressing important problems in simulation model development, analysis, verification, and validation.; The research in this dissertation systematically works through a process of reverse engineering event graphs from simulation code with a target of testing equivalence relationships. The testing of equivalence relationships involves a tight coupling between the static and dynamic properties of a simulation model, where these two classes of properties are handled from the event-oriented world view. This world view can be completely correlated with the other two views, i.e., the activity scanning and process-oriented views. Therefore, the testing of equivalence relationships is comprehensive in this modeling approach, and they are essential for furthering the model development environment. |
