This four-day tutor-led AI in software testing course includes lectures, exercises and practical work, as well as exam preparation. The examination is held a day or so after the course to allow time for revision. It is fully-accredited by UKITB on behalf of ISTQB and has been rated SFIAplus level 3 by the BCS.

The AI Tester course is suitable for those who are, or expect to be, working on projects that have AI at their heart. It is aimed at those who seek a practical application of the core software testing material covered at ISTQB Foundation level on all projects that work with AI.

The Certified Tester AI Testing (CT-AI) qualification is aimed at people who are seeking to extend their understanding of artificial intelligence and/or deep (machine) learning, most specifically testing AI based systems and using AI to test.

Over the 4 days the course will cover:

• Introduction to AI
• Quality Characteristics for AI-Based Systems
• Machine Learning (ML) - Overview
• ML - Data
• ML Functional Performance Metrics
• ML Neural Networks and Testing
• Testing AI-Based Systems - Overview
• Testing AI-Specific Quality Characteristics
• Methods and Techniques for the Testing of AI-Based Systems
• test Environments for Ai-Based Systems
• Using AI for Testing

Yes. This course prepares participants for the ISTQB Foundation - AI For Testers examination.

To qualify as an internationally-recognized Certified Foundation Acceptance Tester and be issued with an ISTQB® AI Foundation Extension Level Certificate, delegates must successfully pass the examination.

• The examination consists of a one-hour exam with 40 multiple choice questions. 
• It will be a ‘closed book’ examination i.e. no notes or books will be allowed into the examination room. 
• Duration of 60 minutes (or 75 minutes for candidates taking examinations that are not in their native language). The pass mark is 65% (26 out of 40). 
• Exam is included in the price

A comprehensive course manual is provided and the course can be tailored to reflect the emphasis required by the customer.

ISTQB Foundation – AI For Testers (a 4-day course)

Course Content

Introduction to AI

• Definition 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 (AIaaS)
• Pre-Trained Models
• Standards, Regulations and AI

Quality Characteristics for AI-Based Systems

• Flexibility and Adaptability
• Autonomy
• Evolution
• Bias
• Ethics
• Side Effects and Reward Hacking
• Transparency, Interpretability and Explainability
• Safety and AI

Machine Learning (ML) - Overview

• Forms of ML
• ML Workflow
• Selecting a Form of ML
• Factors Involved in ML Algorithm Selection
• Overfitting and Underfitting

ML Functional Performance Metrics

• Confusion Matrix
• Add ML Functional Performance Metrics for Classification, Regression and Clustering
• Limitations of ML Functional Performance Metrics
• Selecting ML Functional Performance Metrics
• Benchmark Suites for ML Performance

ML Neural Networks and Testing

• Neural Networks
• Coverage Measures for Neural Networks

Testing AI-Based Systems - Overview

• Specification of AI-Based Systems
• Test Levels for AI-Based Systems
• Test Data for Testing AI-Based Systems
• Testing for Automation Bias in AI-Based Systems
• Documenting an AI Component
• Testing for Concept Drift
• Selecting a Test Approach for an ML System

Testing AI-Specific Quality Characteristics

• Challenges Testing Self-Learning Systems
• Testing Autonomous Self-Learning Systems
• Testing for Algorithmic, Sample and Inappropriate Bias
• Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
• Challenges Testing Complex AI-Based Systems
• Testing Transparency, Interpretability and Explainability of AI-Based Systems
• Test Oracles for AI-Based Systems
• Test Objectives and Acceptance Criteria

Methods and techniques for the Testing of AI-Based Systems

• Adversarial Attacks and Data Poisoning
• Pairwise Testing
• A/B Testing
• Back-to-Back Testing
• Metamorphic Testing (MT)
• Experience-based Testing of AI-Based Systems
• Selecting Test Techniques for AI-Based Systems

Test Environments for AI-Based Systems

• Test Environments for AI-Based Systems
• Virtual Test Environments for Testing AI-Based Systems

Using AI for Testing

• AI Technologies for Testing
• Using AI to Analyse Defect Reports
• Using AI for Test Case Generation
• Using AI for the Optimization of Regression test Suites
• Using AI for Defect Prediction
• using AI for Testing User Interfaces