Origin Tracking + Text Differencing = Textual Model Differencing

R.A. van Rozen, Tijs van der Storm

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)


In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. As a result, the identity 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 identity 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.
Original languageEnglish
Title of host publicationTheory and Practice of Model Transformations - Proceedings of the 8th International Conference on Model Transformation, ICMT 2015, L'Aquila, Italy, July 20-21, 2015
EditorsDimitris Kolovos, Manuel Wimmer
Place of PublicationNew York
Number of pages15
Publication statusPublished - 16 Jul 2015
EventInternational Conference on Model Transformation - L'Aquila, Italy
Duration: 20 Jul 201521 Jul 2015

Publication series



ConferenceInternational Conference on Model Transformation
Abbreviated titleICMT


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