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A Static Analysis of Non-confluent Triple Graph Grammars for Efficient Model Transformation

  • Conference paper
Graph Transformation (ICGT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8571))

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Abstract

Triple Graph Grammars (TGGs) are a well-known bidirectional model transformation language. All actively developed TGG tools pose restrictions to guarantee efficiency (polynomial runtime), without compromising formal properties. Most tools demand confluence of the TGG, meaning that a choice between applicable rules can be freely made without affecting the final result of a transformation. This is, however, a strong restriction for transformations with inherent degrees of freedom that should not be limited at design time. eMoflon is a TGG tool that supports non-confluent TGGs, allowing different results depending on runtime choices. To guarantee efficiency, nonetheless, a local choice of the next source element to be translated, based on source context dependencies of the rules, must not lead to a dead end, i.e., to a state where no rule is applicable for the currently chosen source element to be translated, and the transformation is not yet complete. Our contribution in this paper is to formalize a corresponding property, referred to as local completeness, using graph constraints. Based on the well-known transformation of constraints to application conditions, we present a static analysis that guarantees dead end-freeness for non-confluent TGGs.

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Anjorin, A., Leblebici, E., Schürr, A., Taentzer, G. (2014). A Static Analysis of Non-confluent Triple Graph Grammars for Efficient Model Transformation. In: Giese, H., König, B. (eds) Graph Transformation. ICGT 2014. Lecture Notes in Computer Science, vol 8571. Springer, Cham. https://doi.org/10.1007/978-3-319-09108-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-09108-2_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09107-5

  • Online ISBN: 978-3-319-09108-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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