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CT-AI — AI Testing

ISTQB · Topic 2 of 17

Introduction to AI

AI and ML terminology, AI usage contexts, key factors that distinguish AI systems from conventional software.

6 lessons240 questions
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What this topic covers

  1. 01AI-Specific Quality Characteristics and Testing Techniques480m
  2. 02Follow-up test cases and metamorphic relations in ML testing360m
  3. 03Macro-averaging, Micro-averaging, and Regression Metrics in AI Testing480m
  4. 04Oracle Problem and Metamorphic Testing in AI
  5. 05Source Test Cases, Confusion Matrix Metrics, and ISO/IEC 25059 in AI Testing480m
  6. 06Root Mean Squared Error (RMSE) and Adjusted R-squared in AI Testing

Other topics in CT-AI — AI Testing

Introduction to Artificial Intelligence7 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 lessonsTesting AI-Based Systems Overview6 lessonsTest Environments for AI-Based Systems6 lessonsMachine Learning Development Testing3 lessonsUsing AI for Testing6 lessonsMethods and Techniques for Testing AI6 lessons