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Testing Stochastic AI Models with Hypothesis

PyCon US via YouTube

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Overview

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This talk introduces property-based testing and demonstrates using the Hypothesis library to generate plausible edge cases for stochastic AI models. It covers properties, custom strategies, repeatable random testing, and shrinking.

Syllabus

Intro
About me
Table of Content
Example-based testing- example
Example-based testing - issues
Example-based testing - merge_sort
Property: Commutativity
Property: Invariant functions
Property: The test oracle
Property-based testing
What are the properties in the example?
Metamorphic Testing
Metamorphic Relations
Hypothesis Library
Hypothesis basic strategies
merge_sort test
Define you own strategy
Transforming data functions
Debug hypothesis strategies
Repeatable random testing
Shrinking
Additional Components
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Taught by

PyCon US

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