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CT-AI — AI Testing

ISTQB · Topic 12 of 17

ML Neural Networks and Testing

Coverage measures for neural networks (neuron coverage, sign-change coverage, etc.). Why structural coverage of DNNs is tricky.

6 lessons240 questions
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What this topic covers

  1. 01AI-Specific Quality Characteristics and Testing Neural Networks480m
  2. 02Follow-up test cases and metamorphic relations in ML testing360m
  3. 03Macro-averaging and Micro-averaging in ML Performance Metrics480m
  4. 04Oracle Problem, Metamorphic Testing, and Key AI Quality Metrics480m
  5. 05Source Test Cases, Specificity, Accuracy, and ISO/IEC 25059 in AI Testing480m
  6. 06RMSE, Adjusted R-squared, and Key ML Testing Metrics480m

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 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