Abstract. We use a code generator—type-directed partial evaluation— to verify conversions between isomorphic types, or more precisely to verify that a composite function is the identity function at some complicated type. A typed functional language such as ML provides a natural support to express the functions and type-directed partial evaluation provides a convenient setting to obtain the normal form of their composition. However, off-the-shelf type-directed partial evaluation turns out to yield gigantic normal forms. We identify that this gigantism is due to redundancies, and that these redundancies originate in the handling of sums, which uses delimited continuations. We successfully eliminate these redundancies by extending type-directed partial evaluation with memoization capabilities. The result only works for pure functional programs, but it provides an unexpected use of code generation and it yields orders-of-magnitude improvements both in time and in space for type isomorphisms. 1

Goal-directed evaluation, as embodied in Icon and Snobol, is built on the notions of backtracking and of generating successive results, and therefore it has always been something of a challenge to specify and implement. In this article, we address this challenge using computational monads and partial evaluation. We consider a subset of Icon and we specify it with a monadic semantics and a list monad. We then consider a spectrum of monads that also fit the bill, and we relate them to each other. For example, we derive a continuation monad as a Church encoding of the list monad. The resulting semantics coincides with Gudeman's continuation semantics of Icon. We then compile Icon programs by specializing their interpreter (i.e., by using the first Futamura projection), using type-directed partial evaluation. Through various back ends, including a run-time code generator, we generate ML code, C code, and OCaml byte code. Binding-time analysis and partial evaluation of the continuation-based interpreter automatically give rise to C programs that coincide with the result of Proebsting's optimized compiler.

A generating extension of a program specializes it with respect to some specified part of the input. A generating extension of a program can be formed trivially by applying a partial evaluator to the program; the second Futamura projection describes the automatic generation of non-trivial generating extensions by applying a partial evaluator to itself with respect to the programs. We derive

Abstract These lecture notes describe how to write generating extensions,i.e., dedicated program specializers. The focus is on compositional programs and their associated fold functions. Each generating exten-sion is expressed as an instance of this fold function. A number of examples are considered and pointers to related work are provided. *Lecture notes for Marktoberdorf'99, extended version.