ISTQB AI Testing - Learn best practices and prepare for exam

Course Overview

The testing of traditional systems is well-understood, but AI-based systems, which are becoming more prevalent and critical to our daily lives, introduce new challenges. This course will introduce the key concepts of Artificial Intelligence (AI), how we decide acceptance criteria and how we test AI-based systems. These systems have unique characteristics, which makes them special – they can be complex (e.g. deep neural nets), self-learning, based on big data, and non-deterministic, which creates many new challenges and opportunities for testing them.

The course will introduce the range of types of AI-based systems in use today and explain how machine-learning (ML) is often a key part of these systems and show how easy it is to build ML systems. We will look at how the setting of acceptance criteria needs to change for AI-based systems, why we need to consider ethics, and show how the characteristics of AI-based systems make testing more difficult than for traditional systems.

Introduction to ISTQB AI Testing Course by AIT

Three perspectives are used to show how quality can be achieved with these systems. First, we will consider the choices and checks that need to be made when building a machine-learning system to ensure the quality of data used for both training and prediction. Ideally, we want data that is free from bias and mis-labelling, but, most importantly, closely aligned with the problem. Next, we will consider the range of approaches suitable for the black-box testing of AI-based systems, such as back-to-back testing and A/B testing, introducing, in some detail, the metamorphic testing technique. Third, we will show how white-box testing can be applied to drive the testing and measure the test coverage of neural networks.

The need for virtual test environments will be demonstrated using the case of self-driving cars as an example.

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Finally, the use of AI as the basis of tools that support testing will be considered by looking at examples of the successful application of AI to common testing problems.

The course is highly practical and includes many hands-on exercises, providing attendees with experience of building and testing several different types of machine learning systems. No programming experience is required.