Artificial Intelligence and Software Testing: Building systems you can trust

Front Cover
Half-Title Page
Bcs, The Chartered Institute For It
Title Page
Copyright Page
Contents
List of figures and tables
About the authors
Abbreviations
Glossary
Preface
1. Introduction
	The challenges of testing AI
	Summary
2. AI Trustworthiness and Quality
	Trustworthiness
	AI quality problems
	A model for measuring AI quality
	Regulation of AI quality
	Summary
3. Quality and Bias
	Consequences of the bias definition
	Bias in everyday life
	Unintended bias
	Simpson’s paradox
	Summary
4. Testing Machine Learning Systems
	The role of a tester
	The nature of ML
	Testing metrics
	Testing techniques
	Testing AI-specific characteristics
	Non-deterministic systems
	Transparency, explainability and interpretability
	Summary
5. AI-based Test Automation
	Quality assurance
	Testing versus test automation
	AI in unit 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
	Summary
6. Ontologies for Software Testing
	About ontologies
	Using ontologies for software testing
	Trends in ontology-driven software testing
	Summary
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
	Shifting right into the metaverse
	Evolution, over revolution
	Summary
References
Index