A generative programming approach to developing DSL compilers

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Abstract

Domain-Specific Languages (DSLs) represent a proven approach to raising the abstraction level of programming. They offer high-level constructs and notations dedicated to a domain, structuring program design, easing program writing, masking the intricacies of underlying software layers, and guaranteeing critical properties. On the one hand, DSLs facilitate a straightforward mapping between a conceptual model and a solution expressed in a specific programming language. On the other hand, DSLs complicate the compilation process because of the gap in the abstraction level between the source and target language. The nature of DSLs make their compilation very different from the compilation of common General-Purpose Languages (GPLs). In fact, a DSL compiler generally produces code written in a GPL; low-level compilation is left to the compiler of the target GPL. In essence, a DSL compiler defines some mapping of the high-level information and features of a DSL into the target GPL and underlying layers (e.g., middleware, protocols, objects, ... ). This paper presents a methodology to develop DSL compilers, centered around the use of generative programming tools. Our approach enables the development of a DSL compiler to be structured on facets that represent dimensions of compilation. Each facet can then be implemented in a modular way, using aspects, annotations and specialization. Because these tools are high level, they match the needs of a DSL, facilitating the development of the DSL compiler, and making it modular and re-targetable. We illustrate our approach with a DSL for telephony services. The structure of the DSL compiler is presented, as well as practical uses of generative tools for some compilation facets. © Springer-Verlag Berlin Heidelberg 2005.

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Consel, C., Latry, F., Réveillère, L., & Cointe, P. (2005). A generative programming approach to developing DSL compilers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3676 LNCS, pp. 29–46). https://doi.org/10.1007/11561347_4

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