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BICSI CTAT Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning Neural Networks and Testing | - Neural Networks - Coverage Measures for Neural Networks |
| Topic 2: Testing AI-Specific Quality Characteristics | - Testing Autonomous AI-Based Systems - Test Oracles for AI-Based Systems - Testing the Transparency, Interpretability and Explainability of AI-Based Systems - Challenges Testing Complex AI-Based Systems - Challenges Testing Self-Learning Systems - Testing for Algorithmic, Sample and Inappropriate Bias - Test Objectives and Acceptance Criteria - Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems |
| Topic 3: Using AI for Testing | - Using AI for Defect Prediction - Using AI for Test Case Generation - Using AI for the Optimization of Regression Test Suites - AI Technologies for Testing - Using AI to Analyze Reported Defects - Using AI for Testing User Interfaces
|
| Topic 4: Quality Characteristics for AI-Based Systems | - Evolution - Flexibility and Adaptability - Bias - Transparency, Interpretability and Explainability - Side Effects and Reward Hacking - Safety and AI - Ethics - Autonomy |
| Topic 5: Introduction to AI | - AI Technologies - Pre-Trained Models
- AI-Based and Conventional Systems - AI Development Frameworks - Definition of AI and AI Effect - Hardware for AI-Based Systems - Narrow, General and Super AI - AI as a Service (AIaaS)
|
| Topic 6: Methods and Techniques for Testing AI-Based Systems | - Back-to-Back Testing - Adversarial Attacks and Data Poisoning
- A/B Testing - Pairwise Testing - Metamorphic Testing - Experience-Based Testing of AI-Based Systems
|
| Topic 7: Machine Learning Data | - Data Quality and Its Effect on the ML Model - Training, Validation and Test Datasets in the ML Workflow - Data Preparation as Part of the ML Workflow
|
| Topic 8: Test Environments for AI-Based Systems | - Test Environments for AI-Based Systems - Virtual Test Environments for Testing AI-Based Systems |
| Topic 9: Testing AI-Based Systems Overview | - Specification of AI-Based Systems - Selecting a Test Approach for an ML System - Test Levels for AI-Based Systems
- Testing for Concept Drift - Testing for Automation Bias in AI-Based Systems - Documenting an AI Component |
| Topic 10: Machine Learning (ML) Overview | - Forms of Machine Learning
- Factors Involved in ML Algorithm Selection - Overfitting and Underfitting
|
| Topic 11: Machine Learning Functional Performance Metrics | - Limitations of ML Functional Performance Metrics - Confusion Matrix - Additional ML Functional Performance Metrics for Classification, Regression and Clustering - Selecting ML Functional Performance Metrics - Benchmark Suites for ML |
