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Subtype Polymorphism à la carte via Machine Learning on Dependent Types

Johnson, Colin G., Swan, Jerry, Edwin, Brady (2018) Subtype Polymorphism à la carte via Machine Learning on Dependent Types. In: ML4PL. 2nd International Workshop on Machine Learning Techniques for Programming Languages. . ACM ISBN ACM ISBN 123-4567-24-567/08/06. (doi:10.475/123_4) (KAR id:71461)

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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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.475/123_4
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 30 Dec 2018 17:28 UTC
Last Modified: 16 Feb 2021 14:01 UTC
Resource URI: (The current URI for this page, for reference purposes)
Johnson, Colin G.:
Swan, Jerry:
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