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CT-AI — AI Testing

ISTQB · Topic 13 of 17

Testing AI-Based Systems Overview

Distinctive challenges: oracle problem, non-determinism, drift. Test environments and test data strategy for AI.

6 lessons240 questions
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What this topic covers

  1. 01AI-Specific Quality Characteristics and Testing Overview480m
  2. 02Follow-up test case and invariance under data augmentation480m
  3. 03Macro-averaging, Micro-averaging, and Regression Metrics for AI480m
  4. 04Oracle Problem, Output Quality, and Key Metrics for AI Testing480m
  5. 05Key Metrics and Concepts for AI Testing Overview480m
  6. 06Regression Metrics, Safety, and Testing Techniques for AI480m

Other topics in CT-AI — AI Testing

Introduction to Artificial Intelligence7 lessonsIntroduction to AI6 lessonsMachine Learning Overview6 lessonsInput Data Testing6 lessonsML Data6 lessonsMachine Learning Model Testing7 lessonsML Functional Performance Metrics6 lessonsMachine Learning Functional Performance Metrics6 lessonsTesting Generative AI Systems6 lessonsQuality Characteristics for AI-Based Systems4 lessonsTesting AI-Specific Quality Characteristics6 lessonsML Neural Networks and Testing6 lessonsTest Environments for AI-Based Systems6 lessonsMachine Learning Development Testing3 lessonsUsing AI for Testing6 lessonsMethods and Techniques for Testing AI6 lessons