Affiliation:
1. University of Edinburgh, UK
Abstract
FreezeML is a new approach to first-class polymorphic type inference that
employs term annotations to control when and how polymorphic types are
instantiated and generalised. It conservatively extends Hindley-Milner type
inference and was first presented as an extension to Algorithm W. More modern
type inference techniques such as HM(X) and OutsideIn(X) employ constraints to
support features such as type classes, type families, rows, and other
extensions. We take the first step towards modernising FreezeML by presenting
a constraint-based type inference algorithm. We introduce a new constraint
language, inspired by the Pottier/Rémy presentation of HM(X), in order to allow
FreezeML type inference problems to be expressed as constraints. We present a
deterministic stack machine for solving FreezeML constraints and prove its
termination and correctness.
Publisher
Association for Computing Machinery (ACM)
Subject
Safety, Risk, Reliability and Quality,Software
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3. Frank Emrich Jan Stolarek James Cheney and Sam Lindley. 2022. Constraint-based type inference for FreezeML (extended version). https://doi.org/10.48550/arXiv.2207.09914 arXiv:2207.09914 Frank Emrich Jan Stolarek James Cheney and Sam Lindley. 2022. Constraint-based type inference for FreezeML (extended version). https://doi.org/10.48550/arXiv.2207.09914 arXiv:2207.09914
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