ISTQB Artificial Intelligence (AI) Tester

Become A Certified AI Tester!

The ISTQB Artificial Intelligence (AI) Tester certification extends understanding of quality engineering to AI and/or deep (machine) learning, most specifically testing AI-based systems and using AI in testing.

The course syllabus concentrates on understanding the current state and expected trends of AI to design and execute test cases for AI-based systems.

By the end of the course, you will be able to understand how AI can be used to support software testing. You will also be able to contribute to the test strategy for an AI-based system.

Target Audience

ISTQB AI Tester is designed for:

• Anyone involved in testing AI-based systems and/or AI for testing.

• Testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers.

• Anyone who wants a basic understanding of testing AI-based systems and/or AI for testing.

• Project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants working with an AI-based system.

Course Pre-Requisites

The candidate must hold the ISTQB Foundation certificate to undertake the ISTQB AI Tester course. A minimum of 12 months' testing experience is also recommended.

What You’ll Learn

Learning Outcomes

• Definitions of AI and AI effect, narrow, general and super AI, AI-based and conventional systems. AI technologies, AI development frameworks, hardware for AI-based systems, AI-as-a-Service, pretrained models, standards and regulations.

• AI system flexibility, adaptability, autonomy, evolution, bias, ethics, side effects, transparency, and safety.

• Forms of ML, workflow, forms of ML selection and factors involved, overfitting and underfitting.

• data preparation, validation, quality issues and effects, labelling for learning.

• AI performance metrics – limitations, selection, and benchmarking.

• Neural networks and coverage measures.

• AI-based systems specifications, test levels, test data, automation bias, documentation, concept drifts and test approaches.

• Challenges in testing self learning and autonomous systems, including transparency, interpretability, and explainability. Test objective and acceptance criteria.

• Test methods, techniques and selection for adversarial attacks, pairwise, back-to-back, A/B, metamorphic and experience-based testing.

• Test environments for AI testing.

• Using AI for defect analysis and prediction, test case generation and user interfaces.