Master the skills and knowledge to test AI Based Systems
Become an expert in AI Testing with exclusive materials from world top experts.
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.
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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.
Business Outcomes
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.
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.
Introduction of instructor
Dr Stuart Reid
Dr Stuart Reid is Chief Technology Officer nearly 40 years’ experience in the IT industry, working in development, testing, and education. While currently concentrating on the testing of AI, application areas range from safety-critical to financial and media.
Stuart supports the worldwide testing community in a number of roles.
He is convener of the ISO Software Testing Working Group, which has published the ISO/IEC/IEEE 29119 series of software testing standards and is the co-convener of the ISO Joint Working Group on Testing AI. Stuart previously led the ISO project on autonomous systems for software and systems engineering.
He was also co-founder and first president of the International Software Testing Qualifications Board (ISTQB) to promote software testing qualifications globally and he was one of the authors of the new ISTQB certification on the testing of AI-based systems.
Educational target
The Certified Tester AI Testing training course is aimed at anyone involved in testing AI-based systems and/or AI for testing.
This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This course is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants.
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Career Paths for Testers
The ISTQB® scheme provides support for the definition of career paths in testing by offering a 3-tiered certification scheme starting with the Foundation Level and continuing with the Advanced Level and Expert Level. These are supported by a collection of Agile modules as well as Specialist modules which enable additional specialist skills to be developed in certain subjects, e.g., AI testing.
The Specialist syllabi build on the Foundation Level and establish a platform from which further skills and knowledge may be acquired for different testing topics.
