2. AI TRUSTWORTHINESS AND QUALITY
A model for measuring AI quality
Consequences of the bias definition
4. TESTING MACHINE LEARNING SYSTEMS
Testing AI-specific characteristics
Transparency, explainability and interpretability
Testing versus test automation
AI in UI level test automation
Applying AI to other tasks in software quality assurance
Evaluating tool support for testing
Tasks that will likely remain challenging for AI
6. ONTOLOGIES FOR SOFTWARE TESTING
Using ontologies for software testing
Trends in ontology-driven software testing
7. SHIFTING RIGHT INTO THE METAVERSE WITH DIGITAL TWIN TESTING
The shift-right approach to testing
Cognitive engineering principles
Digital twin concept in shifting right
Case study: helping the community stay safe during the pandemic
Case study: Smart City Data Exchange – testing in the metaverse