Testing and its many guises

Testing is an essential practice across various fields, including manufacturing, education, software development, and machine learning (ML), aimed at ensuring quality and identifying potential flaws before they escalate into costly issues. Despite the imperfections of tests, they remain invaluable for preventing defects, reinforcing knowledge, and detecting software bugs early. In software engineering, rigorous testing frameworks have been developed, while ML testing is less prevalent and typically involves ad hoc scripts for error analysis, leading to errors and biases in deployed models. Unlike traditional software, ML models learn their logic from data and often include stochastic elements, requiring tests to focus on deterministic data components to ensure appropriate learning. The distinction between model evaluation and testing is crucial, as evaluation measures generalization capacity but fails to address specific data subsets or edge cases, necessitating error analysis and ML testing for reliable model deployment. The article explores three ML testing frameworks: confidence tests for assessing performance across data subgroups, invariance tests using synthetic data to check prediction consistency, and counterfactual/adversarial tests to understand and improve model predictions by manipulating input features. Openlayer provides systematic testing tools for ML models, allowing practitioners to create comprehensive tests and increase trust in their models before deployment.