Practical Datatype Specializations with Phantom Types and Recursion Schemes

Authors:
Matthew Fluet;Riccardo Pucella
Affiliations:
Department of Computer Science, Cornell University, Ithaca, NY 14853 USA;Department of Computer Science, Cornell University, Ithaca, NY 14853 USA
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2006

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Abstract

Datatype specialization is a form of subtyping that captures program invariants on data structures that are expressed using the convenient and intuitive datatype notation. Of particular interest are structural invariants such as well-formedness. We investigate the use of phantom types for describing datatype specializations. We show that it is possible to express statically-checked specializations within the type system of Standard ML. We also show that this can be done in a way that does not lose useful programming facilities such as pattern matching in case expressions.