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A Syntax-Directed Model Transformation Framework Based on Attribute Grammars

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Part of the Communications in Computer and Information Science book series (CCIS,volume 563)


Model transformation is a key aspect of model-driven software development because it enables the automatic derivation of different interpretations of a system model. In many scenarios (e.g., design of domain-specific languages), models usually have implicit identifiable primary tree-like syntactic structures, on which additional secondary relationships are imposed to yield the final model graphs. Therefore, in these scenarios it seems natural to address the processing of these models on the basis of their underlying syntactic structure. For this purpose, we have developed AGT, an experimental transformation framework based on attribute grammars, which takes full advantage of the underlying syntactic structure of source models. For models in which this structure is clearly identifiable, the approach could result more natural and easier to use and maintain than other more conventional model transformation approaches (e.g., those based on more standard model transformation languages).


  • Attribute grammar
  • Model-driven development
  • Model transformation

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  • DOI: 10.1007/978-3-319-27653-3_14
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We would like to thank Juan-Pablo Gracia-Benitez by contributing to a preliminary implementation of the framework. This work has been partially supported by the BBVA Foundation (research grant HUM14_251), by the Spanish R&D&I Plan (research grant TIN2014-52010-R), and by Santander-UCM GR3/14 (group number 962022).

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Correspondence to José-Luis Sierra .

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Sarasa-Cabezuelo, A., Sierra, JL. (2015). A Syntax-Directed Model Transformation Framework Based on Attribute Grammars. In: Sierra-Rodríguez, JL., Leal, JP., Simões, A. (eds) Languages, Applications and Technologies. SLATE 2015. Communications in Computer and Information Science, vol 563. Springer, Cham.

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