Zipping Strategies and Attribute Grammars

TL;DR

This paper introduces a unified, purely functional approach to embedding strategic term rewriting and attribute grammars using zippers, enabling easier extension, combination, and application to language processing tasks.

Contribution

It presents a novel zipper-based embedding that combines strategic rewriting and attribute grammars in a concise, maintainable, and extensible library for language engineering.

Findings

Successfully optimized Haskell let expressions

Expressed various Haskell refactorings

Solved language processing tasks in LDTA Challenge

Abstract

Strategic term rewriting and attribute grammars are two powerful programming techniques widely used in language engineering. The former, relies on strategies to apply term rewrite rules in defining language transformations, while the latter is suitable to express context-dependent language processing algorithms. Each of these techniques, however, is usually implemented by its own powerful and large language processor system. As a result, it makes such systems harder to extend and to combine. In this paper, we present the embedding of both strategic tree rewriting and attribute grammars in a zipper-based, purely functional setting. Zippers provide a simple, but generic tree-walk mechanism that is the building block technique we use to express the purely-functional embedding of both techniques. The embedding of the two techniques in the same setting has several advantages: First, we easily combine/zip attribute grammars and strategies, thus providing language engineers the best of the two worlds. Second, the combined embedding is easier to maintain and extend since it is written in a concise and uniform setting. This results in a very small library which is able to express advanced (static) analysis and transformation tasks. We show the expressive power of our library in optimizing Haskell let expressions, expressing several Haskell refactorings and solving several language processing tasks of the LDTA Tool Challenge.