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.

Properties such as logging, persistence, debugging, tracing, distribution, performance monitoring and exception handling occur in most programming paradigms and are normally very difficult or even impossible to modularize with traditional modularization mechanisms because they are cross-cutting. Recently, aspect-oriented programming has enjoyed recognition as a practical solution for separating these concerns. In this paper we describe an extension to the Stratego term rewriting language for capturing such properties. We show our aspect language offers a concise, practical and adaptable solution for dealing with unanticipated algorithm extension for forward data-flow propagation and dynamic type checking of terms. We briefly discuss some of the challenges faced when designing and implementing an aspect extension for and in a rule-based term rewriting system.

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

Attribute grammars are a powerful specification formalism for tree-based computation, particularly for software language processing. Various extensions have been proposed to abstract over common patterns in attribute grammar specifications. These include various forms of copy rules to support non-local dependencies, collection attributes, and expressing dependencies that are evaluated to a fixed point. Rather than implementing extensions natively in an attribute evaluator, we propose attribute decorators that describe an abstract evaluation mechanism for attributes, making it possible to provide such extensions as part of a library of decorators. Inspired by strategic programming, they are specified using generic traversal operators. To demonstrate their effectiveness, we describe how to employ decorators in name, type, and flow analysis.

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.

In previous work, we invented the notion of functional strategies, that is, first-class generic functions which can traverse into terms of any type while mixing uniform and type-specific behaviour. In the present paper, we reconstruct functional strategies in Haskell in an extremely compact style relying on just two combinators: adhoc for type-based function dispatch, and hfoldr for folding over constructor applications. This reconstruction constitutes yet another way to turn Haskell into a (more) generic programming language. Our approach is lightweight (both for language users and implementors) , highly expressive, well-founded, and has already proven its value in important application domains.

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