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CT-AI — AI Testing

ISTQB · Topic 16 of 17

Using AI for Testing

AI applied to testing itself: test-case generation, defect prediction, regression-test selection.

6 lessons240 questions
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What this topic covers

  1. 01AI-specific quality characteristics and fairness testing480m
  2. 02Follow-up test cases and invariance under data augmentation420m
  3. 03Macro-averaging and Micro-averaging for Multi-class Metrics360m
  4. 04Oracle Problem, Output Quality, and Key Metrics for AI Testing480m
  5. 05Source test case and metamorphic testing in AI systems480m
  6. 06RMSE, Adjusted R-squared, and Canary Testing for AI Models600m

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 lessonsTesting AI-Based Systems Overview6 lessonsTest Environments for AI-Based Systems6 lessonsMachine Learning Development Testing3 lessonsMethods and Techniques for Testing AI6 lessons