There are situations in programmingwhere some dynamic typing is needed, even in the presence of advanced static type systems. We investigate the interplay of dynamic types with other advanced type constructions, discussing their integration into languages with explicit polymorphism (in the style of system F ), implicit polymorphism (in the style of ML), abstract data types, and subtyping.

Partial-evaluation folklore has it that massaging one's source programs can make them specialize better. In Jones, Gomard, and Sestoft's recent textbook, a whole chapter is dedicated to listing such "binding-time improvements": nonstandard use of continuationpassing style, eta-expansion, and a popular transformation called "The Trick". We provide a unified view of these binding-time improvements, from a typing perspective. Just as a

Type-directed partial evaluation stems from the residualization of static values in dynamic contexts, given their type and the type of their free variables. Its algorithm coincides with the algorithm for coercing a subtype value into a supertype value, which itself coincides with Berger and Schwichtenberg's normalization algorithm for the simply typed -calculus. Type-directed partial evaluation thus can be used to specialize a compiled, closed program, given its type.

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An important decision when implementing languages with polymorphic types, such as Standard ML or Haskell, is whether to represent data in boxed or unboxed form and when to transform them from one representation to the other. Using a language with explicit representation types and boxing/unboxing operations we axiomatize equationally the set of all explicitly boxed versions, called completions , of a given source program. In a two-stage process we give some of the equations a rewriting interpretation that captures eliminating boxing/unboxing operations without relying on a specific implementation or even the semantics of the underlying language. The resulting reduction systems operate on equivalence classes of completions defined by the remaining equations E, which can be understood as moving boxing/unboxing operations along data flow paths in the source program. We call a completion e opt formally optimal if every other completion for the same program (and at the same representation ty...