Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Artificial Intelligence › Machine Learning & Data › Model Testing

Test case design

concept · part of Model Testing

Test case design for AI/ML systems ensures models behave correctly across a wide range of situations. It identifies data handling errors, model performance failures, scalability problems, and edge cases. Without rigorous design, models may fail in deployment. Key components: input data (normal, edge, error cases), expected output (predictions, error handling, performance metrics), and test conditions (memory usage, processing time, load conditions).

Inside Test case design (8)

• Boundary value analysis — Test case design also includes boundary value analysis, which tests the model's behavior at the edges of its input space.
• Common data testing — Test case design also includes testing with common data inputs the model will encounter regularly.
• Equivalence partitioning — Test case design also employs equivalence partitioning, which divides the input space into partitions where the model is expected to behave similarly.
• Error injection testing — Test case design also includes error injection testing, which introduces intentional errors to observe how the model detects and handles them.
• Evaluation environment — Test case design also includes defining the evaluation environment, which specifies constraints such as memory usage (ensuring the model can handle large datasets without exceeding limits), processing time (meeting acceptable performance criteria), and load conditions (maintaining performance under high loads or when multiple models are deployed simultaneously).
• Performance thresholds — Test case design also includes performance testing with specific thresholds for metrics like accuracy, precision, recall, F1-score, and latency.
• Robustness testing — Test case design also includes robustness testing for less frequent but crucial cases: extreme values far outside the normal range, unseen categories (classes not trained on), and missing or incomplete data.
• Test coverage — Test case design also involves assessing test coverage (how much behavior/code is covered), bug detection rate (how well tests identify bugs), and performance under stress (accuracy and efficiency under edge cases or heavy load).

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