Model Differencing for Textual DSLs
Résumé
—The syntactic and semantic comparison of models is important for understanding and supporting their evolution. In this paper we present TMDIFF, a technique for semanti-cally comparing models that are represented as text. TMDIFF incorporates the referential structure of a language, which is determined by symbolic names and language-specific scoping rules. Furthermore, it employs a novel technique for matching entities existing in source and target versions of a model, and finds entities that are added or removed. As a result, TMDIFF is fully language parametric, and brings the benefits of model differencing to textual languages.
Domaines
Autre [cs.OH]
Origine : Fichiers produits par l'(les) auteur(s)
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