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CT-AI — AI Testing

ISTQB · Topic 11 of 17

Testing AI-Specific Quality Characteristics

Testing for bias, robustness, adversarial inputs, explainability outputs.

6 lessons240 questions
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What this topic covers

  1. 01Testing AI-Specific Quality Characteristics: Robustness, Fairness, and Transparency480m
  2. 02Follow-up test cases and invariance under data augmentation360m
  3. 03Macro-averaging, Micro-averaging, and Regression Metrics for AI Testing480m
  4. 04Oracle Problem, Metamorphic Testing, and RAG Testing for AI Systems480m
  5. 05AI Quality Metrics and Standards: Accuracy, Specificity, Transparency480m
  6. 06Regression Metrics and Deployment Testing for AI Systems480m

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 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