Over the years, testing has become one of the main focus areas in development teams, a good feature is a well tested one. In the field of AI this is many times a real struggle. Since eventually most advanced AI models are stochastic - we can’t manually define all their possible edge cases. This led us to use the hypothesis library which does a lot of that for you, while you can focus on defining the properties and specifications of your system.
In this talk, I will cover shortly the theory of property-based testing and then jump into use cases and examples to demonstrate how we used the hypothesis library to generate random examples of plausible edge cases of our AI model.