Extended Abstract: Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree Models

Inhalt:
Testing 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. A decision tree model especially facilitates MBT for black-box systems if no system reset is possible. A case study on a discrete system illustrates our MBT approach.