This paper describes a technique for assembling both data types and functions from isolated individual components. We also explore how the same technology can be used to combine free monads and, as a result, structure Haskell’s monolithic IO monad. 1

In this paper, we describe two techniques for the efficient, modularized implementation of a large class of algorithms. We illustrate these techniques using several examples, including efficient generic unification algorithms that use reference cells to encode substitutions, and highly modular language implementations. We chose these examples to illustrate the following important techniques that we believe many functional programmers would find useful. First, defining recursive data types by splitting them into two levels: a structure defining level, and a recursive knot-tying level. Second, the use of rank-2 polymorphism inside Haskell’s record types to implement a kind of type-parameterized modules. Finally, we explore techniques that allow us to combine already existing recursive Haskell data-types with the highly modular style of programming proposed here. 1

With ever increasing use of computers in safety and security critical applications, the need for trustworthy computer systems has never been greater. But how can such trust be established? For example, how can we be sure that our computer systems will not destroy or corrupt valuable data, compromise privacy, or trigger

Refactorings are source-to-source program transformations which change program structure and organisation, but not program functionality. Documented in catalogues and supported by tools, refactoring provides the means to adapt and improve the design of existing code, and has thus enabled the trend towards modern agile software development processes. Refactoring has taken a prominent place in software development and maintenance, but most of this recent success has taken place in the OO and XP communities. In our project, we explore the prospects for ‘Refactoring Functional Programs’, taking Haskell as a concrete case-study. This paper discusses the variety of pragmatic and implementation issues raised by our work on the Haskell Refactorer. We briefly introduce the ideas behind refactoring, and a set of elementary functional refactorings. The core of the paper then outlines the main challenges that arise from our aim to produce practical tools for a decidedly non-toy language, summarizes our experience in trying to establish the necessary meta-programming infrastructure and gives an implementation overview of our current prototype refactoring tool. Using Haskell as our implementation language, we also offer some preliminary comments on Haskell programming-in-the-large.

Abstract. Whatever programming paradigm for data processing we choose, data has the tendency to live on the other side or to eventually end up there. The major paradigms for data processing are Cobol, object, relational and XML; each paradigm offers many facets and many versions; each paradigm provides specific forms of data models (object models, relational schemas, XML schemas, etc.). Each data-processing application depends on a horde of interrelated data models and artifacts that are derived from data models (such as data-access layers). Such conglomerations of data models are challenging due to paradigmatic impedance mismatches, performance requirements, loose-coupling requirements, and others. This ubiquitous problem calls for a good understanding of techniques for mappings between data models, actual data, and operations on data. This tutorial lists and discusses mapping scenarios, mapping techniques, impedance mismatches and research challenges regarding mappings.

This paper presents a monadic approach to incremental computation, suitable for purely functional languages such as Haskell. A program that uses incremental computation is able to perform an incremental amount of computation to accommodate for changes in input data. Recently, Acar, Blelloch and Harper presented a small Standard ML library that supports efficient, high-level incremental computations [1]. Here, we present a monadic variant of that library, written in Haskell extended with first-class references. By using monads, not only are we able to provide a purely functional interface to the library, the types also enforce "correct usage" without having to resort to any type-system extension. We also find optimization opportunities based on standard monadic combinators.

Previous implementations of generic rewriting libraries have a number of limitations: they require the user to either adapt the datatype on which rewriting is applied, or the rewriting rules are specified as functions, which makes it hard or impossible to document, test, and analyse them. We describe a library that demonstrates how to overcome these limitations by defining rules in terms of datatypes, and show how to use a type-indexed datatype to automatically extend a datatype for syntax trees with a case for metavariables. We then show how rewrite rules can be implemented without any knowledge of how the datatype is extended with metavariables. We use Haskell, extended with associated type synonyms, to implement both type-indexed datatypes and generic functions. We analyse the performance of our library and compare it with other approaches to generic rewriting.