Behavioral Testing of ML models is a systematic approach to testing model behavior using input-output test cases — inspired by software engineering testing practices, organizing tests into capability-specific categories to comprehensively evaluate model reliability.

CheckList Framework

• Minimum Functionality Tests (MFT): Simple test cases that every model should handle correctly.
• Invariance Tests (INV): Perturbations that should NOT change the prediction.
• Directional Expectation Tests (DIR): Perturbations that should change the prediction in a known direction.
• Test Generation: Use templates, perturbation functions, and generative models to create test suites.

Why It Matters

• Beyond Accuracy: Accuracy on a test set doesn't reveal specific failure modes — behavioral tests do.
• Systematic Coverage: Tests cover linguistic capabilities, robustness, fairness, and domain-specific requirements.
• Regression Testing: Behavioral test suites catch regressions when models are retrained or updated.

Behavioral Testing is test-driven development for ML — systematically testing model capabilities, invariances, and directional expectations.