Artificial Intelligence and Software Testing

Table of contents

1. Front Cover
2. Half-Title Page
3. Bcs, The Chartered Institute For It
4. Title Page
5. Copyright Page
6. Contents
7. List of figures and tables
8. About the authors
9. Abbreviations
10. Glossary
11. Preface
12. 1. Introduction
    1. The challenges of testing AI
    2. Summary
13. 2. AI Trustworthiness and Quality
    1. Trustworthiness
    2. AI quality problems
    3. A model for measuring AI quality
    4. Regulation of AI quality
    5. Summary
14. 3. Quality and Bias
    1. Consequences of the bias definition
    2. Bias in everyday life
    3. Unintended bias
    4. Simpson’s paradox
    5. Summary
15. 4. Testing Machine Learning Systems
    1. The role of a tester
    2. The nature of ML
    3. Testing metrics
    4. Testing techniques
    5. Testing AI-specific characteristics
    6. Non-deterministic systems
    7. Transparency, explainability and interpretability
    8. Summary
16. 5. AI-based Test Automation
    1. Quality assurance
    2. Testing versus test automation
    3. AI in unit test automation
    4. AI in UI level test automation
    5. Applying AI to other tasks in software quality assurance
    6. Evaluating tool support for testing
    7. Tasks that will likely remain challenging for AI
    8. Summary
17. 6. Ontologies for Software Testing
    1. About ontologies
    2. Using ontologies for software testing
    3. Trends in ontology-driven software testing
    4. Summary
18. 7. Shifting Right into the Metaverse with Digital Twin Testing
    1. The shift-right approach to testing
    2. Cognitive engineering principles
    3. Digital twin concept in shifting right
    4. Case study: helping the community stay safe during the pandemic
    5. Case study: Smart City Data Exchange – testing in the metaverse
    6. Shifting right into the metaverse
    7. Evolution, over revolution
    8. Summary
19. References
20. Index