Testing the Performance of Complex System Simulations
in the Production Automation Domain
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Production automation systems are often complex systems as the behavior of the overall system cannot easily be predicted from the behavior of the subsystems. Thus simulation is used to study the behavior of complex production automation systems. In addition to the accuracy of the simulation the system performance is an important issue, particularly if many parameter variants for system behavior are to be tested. Parameters for assembly lines are, for instance, scheduling strategy and failure handling strategy. SW testing investigates the quality of the product or service under test. The goal i...
Production automation systems are often complex systems as the behavior of the overall system cannot easily be predicted from the behavior of the subsystems. Thus simulation is used to study the behavior of complex production automation systems. In addition to the accuracy of the simulation the system performance is an important issue, particularly if many parameter variants for system behavior are to be tested. Parameters for assembly lines are, for instance, scheduling strategy and failure handling strategy. SW testing investigates the quality of the product or service under test. The goal is to generate test cases in a automated and systematic way to find suitable scenarios for most of the requirements in a short period of time. In this thesis, the test cases define input data to simulate complex distributed assembly line systems by means of a simulation tool. The thesis presents an effective method for testing the performance of test case generator approaches. The evaluation part explores how the ontology-based approach reduces costs for test description, enables a definable test coverage, and increases expandability due to lower efforts to implement new test case parameters.
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