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

Citation
26. Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2023)
Contribution to Conference
26. Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen, MBMV 2023  
Scopus ID
2-s2.0-85167446440
ISBN
9783800760664
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.
Subjects
MLE@TUHH