AI and Software Testing

This AI & Software testing Course is a two-day course developing skills to perform test analysis, test design, test implementation, test execution, and test completion activities for a system that integrates one (or more) AI-based components.

Learners will also have the ability to AI support testing activities and contribute to the evaluation of AI-based testing approaches and related tooling in the context of their organization.

The course uses the training materials from the A4Q AI & Software Testing Foundation, but is shortened to two days, covering all of the necessary concepts, but requiring some self-study to prepare for the A4Q exam.

Target Audience

This course is aimed at anyone who wants to understand the theory of AI and software testing and what the value of AI can be in a software testing environment. This includes:

  • Software Testers

  • Software Engineers

  • Designers

  • Anyone involved in coaching or training teams seeking to understand AI in software testing

  • Managers seeking to understand how AI can add value in their organization

  • Business Analysts working seeking to understand the value of AI to the business 

About the trainer

Adam Leon Smith has two decades of experience in technology and has held senior roles at Barclays and Deutsche Bank delivering large complex transformation projects. 

Adam Leon Smith-1

He is now CTO of Dragonfly, an international consultancy focused on software testing and quality assurance, with a particular focus on AI.  Adam is the current chair of the British Computer Society’s Special Interest Group in Software Testing and he is also an ISO/IEC convenor and editor, and is involved in the development of numerous international standards relating to AI and quality.  He is a member of the European Commission’s AI Alliance, and the IEEE’s group developing international standards on technology ethics.



Course Content

Key Aspects of Artificial Intelligence: Human Intelligence and Artificial Intelligence; History of AI; Symbolic AI; Sub-symbolic AI; Some ML Algorithms in More Detail; Applications and Limits of AI.

Testing Artificial Intelligence Systems: General Problems with Testing AI Systems; Machine Learning Model Training and Testing; AI Test Environments; Strategies to Test AI-based Systems; Metrics for Testing AI-based Systems.

Using AI to Support Testing: AI in Testing; Applying AI to Testing Tasks and Quality Management; AI in Component Level Test Automation; AI in Integration Level or System Level Test Automation; AI-based Tool Support for Testing           


There are no specific entry requirements to be eligible for this course, however a level of experience or training in software testing is recommended.