Ontology-Based Test Case Generation For Simulating Complex Production Automation Systems

The behavior of complex production automation systems is hard to predict, therefore simulation is used to study the likely system behavior. However, in a real-world system many parameter variants need to be tested with limited resources. Therefore, test cases need to be generated in a systematic way to find suitable scenarios efficiently. This paper investigates the effort of two approaches for providing test cases based on available testing knowledge. The traditional approach uses a static generator script based on implicit testing knowledge, which takes significant effort to add new parameters. The innovative approach uses a dynamic generic generator script based on an ontology data model of the testing knowledge. We empirically evaluate these approaches with a use case from the production automation domain. Major result is that the high-level test description of the ontology-based approach takes more initial effort for setup, but increases the usability and reduces the risk of errors during the test case generation process.