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

End Page
84
Article Number
85
Citation
26. Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2023); 84-85
Contribution to Conference
26. Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen, MBMV 2023  
Scopus ID
2-s2.0-85167446440
Publisher
VDE Verglag GmbH
ISBN
978-3-8007-6066-4
978-3-8007-6065-7
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
DDC Class
600: Technology