Origin Tracking + Text Differencing = Textual Model Differencing

Abstract : In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff (TMDIFF), a technique to support model differencing for textual languages. TMDIFF requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, TMDIFF can then determine which elements are the same across revisions, and which are added or removed. As a result, TMDIFF brings the benefits of model differencing to textual languages.
Type de document :
Communication dans un congrès
8th International Conference, ICMT 2015, Held as Part of STAF 2015, Jul 2015, L'Aquila, Italy. Springer International Publishing, pp.18 - 33, 2015, Theory and Practice of Model Transformations
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https://hal.inria.fr/hal-01261479
Contributeur : Tijs Van Der Storm <>
Soumis le : lundi 25 janvier 2016 - 14:06:39
Dernière modification le : vendredi 29 janvier 2016 - 13:31:17

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  • HAL Id : hal-01261479, version 1

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R. Rozen, Tijs Van Der Storm. Origin Tracking + Text Differencing = Textual Model Differencing. 8th International Conference, ICMT 2015, Held as Part of STAF 2015, Jul 2015, L'Aquila, Italy. Springer International Publishing, pp.18 - 33, 2015, Theory and Practice of Model Transformations. 〈hal-01261479〉

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