In this paper, we explore an extension to Haskell type classes that allows a type class declaration to define data types as well as values (or methods). Similarly, an instance declaration gives a witness for such data types, as well as a witness for each method. It turns out that this extension directly supports the idea of a type-indexed type, and is useful in many applications, especially for self-optimising libraries that adapt their data representations and algorithms in a type-directed manner.

Abstract. Strafunski is a Haskell-centred software bundle for implementing language processing components — most notably program analyses and transformations. Typical application areas include program optimisation, refactoring, software metrics, software re- and reverse engineering. Strafunski started out as generic programming library complemented by generative tool support to address the concern of generic traversal over typed representations of parse trees in a scalable manner. Meanwhile, Strafunski also encompasses means of integrating external components such as parsers, pretty printers, and graph visualisation tools. In a selection of case studies, we demonstrate that typed functional programming in Haskell, augmented with Strafunski’s support for generic traversal and external components, is very appropriate for the development of practical language processors. In particular, we discuss using Haskell for Cobol reverse engineering, Java code metrics, and Haskell re-engineering.

Strategic programming is a generic programming idiom for processing compound data such as terms or object structures. At the heart of the approach is the separation of two concerns: basic dataprocessing computations vs. traversal schemes. Actual traversals are composed by passing the former as arguments to the latter. Traversal schemes can be defined by the strategic programmer using a combinator style that relies on primitives for layered traversal. In this paper, we take a look at strategic programming from an aspect-oriented programming perspective. Throughout the paper, we compare strategic programming with adaptive programming, which is a well-established aspectual approach to the traversal of object structures. We start from the observation that aspect-oriented programming terms, e.g., crosscutting, join point, and advice can be instantiated for aspectual traversal approaches.

Abstract. Strategic programming is generic programming with the use of strategies. A strategy is a generic data-processing action which can traverse into heterogeneous data structures while mixing uniform and type-specific behaviour. With strategic programming, one gains full control over the application of basic actions, most notably full traversal control. Using a combinator style, traversal schemes can be defined, and actual traversals are obtained by passing the problem-specific ingredients as parameters to suitable schemes. The prime application domain for strategic programming is program transformation and analysis. In this paper, we provide a language-independent definition that generalises over existing incarnations of this idiom in term rewriting, functional programming, and object-oriented programming.

In many strategic systems, the choice combinator provides a powerful mechanism for controlling the application of rules and strategies to terms. The ability of the choice combinator to exercise control over rewriting is based on the premise that the success and failure of strategy application can be observed. In this paper we present a higher-order strategic framework with the ability to dynamically construct strategies containing the choice combinator. To this framework, a combinator called hide is introduced that prevents the successful application of a strategy from being observed by the choice combinator. We then explore the impact of this new combinator on a real-world problem involving a restricted implementation of the Java Virtual Machine.

Traversal strategies provide an established means of describing automated queries, analyses, transformations, and other non-trivial computations on deeply structured data (including, most notably, data representations of software artifacts such as programs). The resulting traversal programs are prone to programming errors. We are specifically concerned with errors that go beyond classic type errors, in particular: (i) divergence of traversal, (ii) unintentional extent of traversal into data, (iii) trivial traversal results, (iv) inapplicability of the constituents of a traversal program along traversal. We deliver a taxonomy of programming errors, and start attacking some of them by refinements of traversal programming.

Autowrite is an experimental software tool written in Common Lisp for handling term rewrite systems and bottom-up tree automata. A graphical interface has been written using McCLIM, (the free implementation of the CLIM specification) in order to free the user of any Lisp knowledge. Software and documentation can be found at Autowrite was initially designed to check call-by-need properties of term rewrite systems. For this purpose, it implements the tree automata constructions used in [10,3,5,13] and many useful operations on terms, term rewrite systems and tree automata. In the first version of Autowrite [4], only the call-by-need properties and a few other simple properties were available from the graphical interface. This new version of Autowrite includes many new functionalities. There are new functionalities related to TRSs, but the most interesting new feature is the possibility to directly handle (load, save, combine with boolean operations) bottom-up tree automata. In addition, we have added on-line timing information. Since the first version the run-times have been considerably improved due to better choices of data structures. The first version of Autowrite was used to check call-by-need for most of the examples presented in [6]. Most of the time no alternative proofs exists. The new features allowed testing many properties of examples presented in [7] for which no easy proof can be written.