Polymorphic type inference and assignment

Abstract : We present a new approach to the polymorphic typing of data accepting in-place modification in ML-like languages. This approach is based on restrictions over type generalization, and a refined typing of functions. The type system given here leads to a better integration of imperative programming style with the purely applicative kernel of ML. In particular, generic functions that allocate mutable data can safely be given fully polymorphic types. We show the soundness of this type system, and give a type reconstruction algorithm.
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Xavier Leroy, Pierre Weis. Polymorphic type inference and assignment. POPL 1991: 18th symposium Principles of Programming Languages, ACM, Jan 1991, Orlando, United States. pp.291-302, ⟨10.1145/99583.99622⟩. ⟨hal-01499974⟩

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