Plambeck, SwantjeSwantjePlambeckFey, GörschwinGörschwinFey2023-07-272023-07-272023-0526th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS 2023)9798350332773https://hdl.handle.net/11420/42350Testing of black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables model-based testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. In this scope, we define a test coverage metric for decision tree models. Furthermore, we identify different modes of testing and explain that a decision tree model especially facilitates model-based testing for black-box systems with limited controllability of inputs and the inability to reset the system to a specific state. A case study on a discrete system illustrates our MBT approach.enAutomatic Test GenerationDecision TreesModel LearningTest CoverageMLE@TUHHConference Paper10.1109/DDECS57882.2023.10139633Conference Paper