Soft type systems provide the benefits of static type checking for dynamically typed languages without rejecting untypable programs. A soft type checker infers types for variables and expressions and inserts explicit run-time checks to transform untypable programs to typable form. We describe a practical soft type system for R4RS Scheme. Our type checker uses a representation for types that is expressive, easy to interpret, and supports efficient type inference. Soft Scheme supports all of R4RS Scheme, including procedures of fixed and variable arity, assignment, continuations, and top-level definitions. Our implementation is available by anonymous FTP. The first author was supported in part by the United States Department of Defense under a National Defense Science and Engineering Graduate Fellowship. y The second author was supported by NSF grant CCR-9122518 and the Texas Advanced Technology Program under grant 003604-014. 1 Introduction Dynamically typed languages like Scheme...

How canweintegrate interaction into a purely declarative language? This tutorial describes a solution to this problem based on a monad. The solution has been implemented in the functional language Haskell and the declarative language Escher. Comparisons are given to other approaches to interaction based on synchronous streams, continuations, linear logic, and side effects.

Mixin modules are proposed as a new construct for module languages, allowing recurslye definitions to span module boundaries. Mixin modules are proposed specifically for the Standard ML language. Several applications are described, including the resolution of cycles in module import dependency graphs, as well as functionality related to Haskell type classes and CLOS generic functions, though without any complications to the core language semantics. Mixin modules require no changes to the core ML type system, and only a very minor change to its run-time semantics. A type system and reduction semantics are provided, and the former is verified to be sound relative to the latter.

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Denotational semantics [Sch86] is a powerful framework for describing programming languages; however, its descriptions lack modularity: conceptually independent language features influence each others' semantics. We address this problem by presenting a theory of modular denotational semantics. Following Mosses [Mos92], we divide a semantics into two parts, a computation ADT and a language ADT (abstract data type). The computation ADT represents the basic semantic structure of the language. The language ADT represents the actual language constructs, as described by a grammar. We define the language ADT using the computation ADT; in fact, language constructs are polymorphic over many different computation ADTs. Following Moggi [Mog89a], we build the computation ADT from composable parts, using monads and monad transformers. These techniques allow us to build many different computation ADTs, and, since our language constructs are polymorphic, many different language semantics. We autom...

Traditional denotational semantics assigns radically different meanings to one and the same phrase depending on the rest of the programming language. If the language is purely functional, the denotation of a numeral is a function from environments to integers. But, in a functional language with imperative control operators, a numeral denotes a function from environments and continuations to integers. This paper introduces a new format for denotational language specifications, extended direct semantics, that accommodates orthogonal extensions of a language without changing the denotations of existing phrases. An extended direct semantics always maps a numeral to the same denotation: the injection of the corresponding number into the domain of values. In general, the denotation of a phrase in a functional language is always a projection of the denotation of the same phrase in the semantics of an extended language---no matter what the extension is. Based on extended direct semantics, i...

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TinkerType is a pragmatic framework for compact and modular description of formal systems (type systems, operational semantics, logics, etc.). A family of related systems is broken down into a set of clauses – individual inference rules – and a set of features controlling the inclusion of clauses in particular systems. Simple static checks are used to help maintain consistency of the generated systems. We present TinkerType and its implementation and describe its application to two substantial repositories of typed lambda-calculi. The first repository covers a broad range of typing features, including subtyping, polymorphism, type operators and kinding, computational effects, and dependent types. It describes both declarative and algorithmic aspects of the systems, and can be used with our tool, the TinkerType Assembler,to generate calculi either in the form of typeset collections of inference rules or as executable ML typecheckers. The second repository addresses a smaller collection of systems, and provides modularized proofs of basic safety properties.

The impact of Domain Specific Languages (DSLs) on software design is considerable. They allow programs to be more concise than equivalent programs written in a high-level programming languages. They relieve programmers from making decisions about data-structure and algorithm design, and thus allows solutions to be constructed quickly. Because DSL's are at a higher level of abstraction they are easier to maintain and reason about than equivalent programs written in a highlevel language, and perhaps most importantly they can be written by domain experts rather than programmers. The problem is that DSL implementation is costly and prone to errors, and that high level approaches to DSL implementation often produce inefficient systems. By using two new programming language mechanisms, program staging and monadic abstraction, we can lower the cost of DSL implementations by allowing reuse at many levels. These mechanisms provide the expressive power that allows the construction of many compil...

Accounts of semantic phenomena often involve extending types of meanings and revising composition rules at the same time. The concept of monads allows many such accounts---for intensionality, variable binding, quantification and focus---to be stated uniformly and compositionally.