Ml Model Testing
ML model testing verifies that a trained machine learning model satisfies defined functional, performance, fairness, robustness, and reliability requirements.
“A test level that focuses on the ability of an ML model to meet required ML functional performance criteria and non-functional criteria.”
(Definition reproduced from the ISTQB Glossary. Copyright belongs to ISTQB.)
View the complete ISTQB GlossaryWhat is Ml Model Testing?
Testing evaluates the model using representative datasets and appropriate statistical metrics before deployment and throughout its operational lifecycle.
Functional evaluation: Measures prediction quality using defined metrics.
Robustness: Verifies behavior with unexpected or noisy inputs.
Bias assessment: Tests for fairness across relevant groups when appropriate.
Real World Example
A regulated product team is preparing a release where Ml model testing appears in reviews, test design conversations, or defect triage rather than as an isolated glossary word.
The risk is that the team treats Ml model testing as interchangeable with nearby ISTQB terms. That makes test scope blurry and can lead to weak evidence for the release decision.
The tester anchors the discussion in the official definition, asks where the concept appears in the product, and designs examples that show the difference between Ml model testing and similar ideas.
The page becomes useful in practice because Ml model testing is connected to a specific testing decision, not memorized as a detached definition.
Practice Questions
Question 1
Which statement BEST describes Ml model testing in the context of ISTQB terminology?
Question 2
A tester needs to explain Ml model testing to a non‑technical stakeholder. Which approach is MOST appropriate?
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