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Incremental Model Transformation for the Evolution of Model-Driven Systems

  • David Hearnden
  • Michael Lawley
  • Kerry Raymond
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4199)

Abstract

Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation.

Keywords

Source Model Logic Language Transformation Language Success Node Resolution Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Hearnden
    • 1
  • Michael Lawley
    • 2
  • Kerry Raymond
    • 2
  1. 1.School of ITEEUniversity of QueenslandAustralia
  2. 2.Queensland University of TechnologyAustralia

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