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 live examples to demonstrate how we used the hypothesis library to generate random examples of plausible edge cases of our AI model.