Subtype Polymorphism à la carte via Machine Learning on Dependent Types

The ability to write 'closed' frameworks in terms of abstract supertypes and subsequently extend them via contractually-conforming subtypes is a ubiquitous programming paradigm (e.g. underpinning Object-Orientation). While the motivation for such abstraction is to insulate against requirements change, any change of contract requires extensive (typically manual) refactoring, potentially throughout the entire class hierarchy. As an alternative to defining such abstractions a priori, we describe the broad role that Machine Learning can play in inducing abstractions from a pre-existing codebase. Concrete examples are given in which contacts are enforced by dependent types in the Idris language.