Abstract. Refactoring is the process of improving the design of existing programs without changing their functionality. These notes cover refactoring in functional languages, using Haskell as the medium, and introducing the HaRe tool for refactoring in Haskell. 1

A heterogeneous collection is a datatype that is capable of storing data of different types, while providing operations for look-up, update, iteration, and others. There are various kinds of heterogeneous collections, differing in representation, invariants, and access operations. We describe HLIST — a Haskell library for strongly typed heterogeneous collections including extensible records. We illustrate HLIST’s benefits in the context of type-safe database access in Haskell. The HLIST library relies on common extensions of Haskell 98. Our exploration raises interesting issues regarding Haskell’s type system, in particular, avoidance of overlapping instances, and reification of type equality and type unification.

Refactorings are source-to-source program transformations which change program structure and organisation, but not program func-tionality. Documented in catalogues and supported by tools, refac-toring provides the means to adapt and improve the design of ex-isting code, and has thus enabled the trend towards modern agile software development processes. Refactoring has taken a promi-nent 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 Func-tional 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 chal-lenges that arise from our aim to produce practical tools for a de-cidedly 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.

Domain specific languages (DSLs) are increasingly used today. Coping with complex language definitions, evolving them in a structured way, and ensuring their error freeness are the main challenges of DSL design and implementation. The use of modular language definitions and composition operators are therefore inevitable in the independent development of language components. In this article, we discuss these arising issues by describing a framework for the compositional development of textual DSLs and their supporting tools. We use a redundance-free definition of a readable concrete syntax and a comprehensible abstract syntax as both representations significantly overlap in their structure. For enhancing the usability of the abstract syntax, we added concepts like associations and inheritance to a grammarbased definition in order to build up arbitrary graphs (as known from metamodeling). Two modularity concepts, grammar inheritance and embedding, are discussed. They permit compositional language definition and thus simplify the extension of languages based on already existing ones. We demonstrate that compositional engineering of new languages is a useful concept when project-individual DSLs with appropriate tool support are defined.

Abstract. The paper “Scrap your boilerplate ” (SYB) introduces a combinator library for generic programming that offers generic traversals and queries. Classically, support for generic programming consists of two essential ingredients: a way to write (type-)overloaded functions, and independently, a way to access the structure of data types. SYB seems to lack the second. As a consequence, it is difficult to compare with other approaches such as PolyP or Generic Haskell. In this paper we reveal the structural view that SYB builds upon. This allows us to define the combinators as generic functions in the classical sense. We explain the SYB approach in this changed setting from ground up, and use the understanding gained to relate it to other generic programming approaches. Furthermore, we show that the SYB view is applicable to a very large class of data types, including generalized algebraic data types. 1

governmental, scientific, and private data. Because they have been standardized and are widely used, many reliable, efficient, and convenient tools for processing data in these formats are readily available. For instance, your favorite programming language undoubtedly has libraries for parsing XML and HTML as well as reading and transforming images in JPEG or movies in MPEG. Query engines are available for querying XML documents. Widely-used applications like Microsoft Word and Excel automatically translate documents between HTML and other standard formats. In short, life is good when working with standard data formats. In an ideal world, all data would be in such formats. In reality, however, we are not nearly so fortunate. An ad hoc data format is any non-standard data format. Typically, such formats do not have parsing, querying, analysis, or transformation tools readily available. Every day, network administrators, financial analysts, computer scientists, biologists, chemists, astronomers, and physicists deal with ad hoc data in a myriad of complex formats. Figure 1 gives a partial sense of the range and pervasiveness of such data. Since off-the-shelf tools for processing these ad hoc data formats do not exist or are not readily available, talented scientists, data analysts, and programmers must waste their time on low-level chores like parsing and format translation to extract the valuable information they need from their data.

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We present a closed dependent type theory whose inductive types are given not by a scheme for generative declarations, but by encoding in a universe. Each inductive datatype arises by interpreting its description—a first-class value in a datatype of descriptions. Moreover, the latter itself has a description. Datatype-generic programming thus becomes ordinary programming. We show some of the resulting generic operations and deploy them in particular, useful ways on the datatype of datatype descriptions itself. Surprisingly this apparently self-supporting setup is achievable without paradox or infinite regress. 1.

A type-indexed function is a function that is defined for each member of some family of types. Haskell's type class mechanism provides collections of open type-indexed functions, in which the indexing family can be extended by defining a new type class instance but the collection of functions is fixed. The purpose of this paper is to present TypeCase: a design pattern that allows the definition of closed type-indexed functions, in which the index family is fixed but the collection of functions is extensible. It is inspired by Cheney and Hinze's work on lightweight approaches to generic programming. We generalise their techniques as a design pattern. Furthermore, we show that type-indexed functions with typeindexed types, and consequently generic functions with generic types, can also be encoded in a lightweight manner, thereby overcoming one of the main limitations of the lightweight approaches.

Massive amounts of useful data are stored and processed in ad hoc formats for which common tools like parsers, printers, query engines and format converters are not readily available. In this paper, we explain the design and implementation of PADS/ML, a new language and system that facilitates the generation of data processing tools for ad hoc formats. The PADS/ML design includes features such as dependent, polymorphic and recursive datatypes, which allow programmers to describe the syntax and semantics of ad hoc data in a concise, easy-to-read notation. The PADS/ML implementation compiles these descriptions into ML structures and functors that include types for parsed data, functions for parsing and printing, and auxiliary support for user-specified, format-dependent and format-independent tool generation.