On Decidability of Nominal Subtyping with Variance

Loading...
Thumbnail Image
Penn collection
Departmental Papers (CIS)
Degree type
Discipline
Subject
Computer Sciences
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Kennedy, Andrew J
Contributor
Abstract

We investigate the algorithmics of subtyping in the presence of nominal inheritance and variance for generic types, as found in Java 5, Scala 2.0, and the .NET 2.0 Intermediate Language. We prove that the general problem is undecidable and characterize three different decidable fragments. From the latter, we conjecture that undecidability critically depends on the combination of three features that are not found together in any of these languages: contravariant type constructors, class hierarchies in which the set of types reachable from a given type by inheritance and decomposition is not always finite, and class hierarchies in which a type may have multiple supertypes with the same head constructor. These results settle one case of practical interest: subtyping between ground types in the .NET intermediate language is decidable; we conjecture that our proof can also be extended to show full decidability of subtyping in .NET. For Java and Scala, the decidability questions remain open; however, the proofs of our preliminary results introduce a number of novel techniques that we hope may be useful in further attacks on these questions.

Advisor
Date of presentation
2006-09-01
Conference name
Departmental Papers (CIS)
Conference dates
2023-05-17T07:15:10.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Andrew J. Kennedy and Benjamin C. Pierce. On Decidability of Nominal Subtyping with Variance, September 2006. FOOL-WOOD '07. ACM COPYRIGHT NOTICE. Copyright © 2006 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.
Recommended citation
Collection