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.