Название: Introduction to AI Testing: Guide to ISTQB CT-AI Certification
Автор: Iosif Itkin, Iuliia Emelianova, Dmitrii Degtiarenko, Anna-Maria Lukina
Издательство: BCS, the Chartered Institute for IT
Год: 2025
Страниц: 340
Язык: английский
Формат: epub (true)
Размер: 25.2 MB

This book offers a comprehensive introduction to Artificial Intelligence and Machine Learning fundamentals, equipping software testers with the skills to effectively leverage AI-powered solutions for testing complex systems and AI applications. It also fully prepares readers for the ISTQB Certified Tester AI Testing (CT-AI) certification exam.

Written in a practical and accessible style, this book offers a structured approach to AI testing based on the ISTQB CT-AI syllabus. It includes practical examples, detailed explanations, industry insights, a mock exam and chapter-based questions to help readers succeed in the ISTQB CT-AI exam.

This book is designed for newcomers and seasoned practitioners involved in testing AI-based systems as well as those seeking a deeper understanding of AI systems in general. It offers valuable insights into implementing AI-enabled solutions and enhancing the quality of AI-driven software, making it an indispensable resource.

PART 1 – INTRODUCTION TO AI AND AI-BASED SYSTEMS
This part lays the foundation by introducing the core concepts of AI and how AI-based systems differ from traditional systems. Covering Chapters 1 and 2, it explains what AI is, explores its main types and technologies and outlines essential topics such as pre-trained models, transfer learning and AI as a service. It also addresses important system characteristics, such as flexibility, autonomy, bias and ethical considerations, setting the stage for understanding how these elements influence the design and behaviour of AI-based systems.

PART 2 – MACHINE LEARNING
Chapters 3 to 6 form the second part of the book, which focuses on machine learning, the backbone of most AI-based systems. This part explores different forms of ML (supervised, unsupervised and reinforcement learning), the ML development workflow and how data quality affects model performance. It also covers performance metrics and challenges such as overfitting and underfitting, and introduces neural networks. The goal of this part is to provide a solid grasp of how ML models are developed, trained and evaluated, which is essential for testing AI-based systems effectively.

PART 3 – TESTING AI-BASED SYSTEMS
Part 3, consisting of Chapters 7 to 10, zooms in on the specifics of how AI-based systems should be tested. It introduces various testing levels and methods tailored to AI-specific features, including dealing with algorithmic bias, non-determinism and lack of transparency. You will also learn about appropriate testing techniques such as metamorphic testing, A/B testing and back-to-back testing. In addition, this part discusses how to select test approaches and techniques, how to specify and document AI components and how to create suitable test environments for AI-based systems.

PART 4 – TESTING WITH AI
The final part of the book, Chapter 11, shifts focus from testing AI to using AI as a tool for testing. It explores how AI technologies can support and enhance traditional testing activities, such as defect prediction, test case generation, regression suite optimisation and user interface testing. This chapter demonstrates how AI can become a valuable part of the tester’s toolkit, making testing processes smarter, faster and more adaptive.

ISTQB® is a registered trademark of the International Software Testing Qualifications Board.

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