A Static Semantics for Alloy and its Impact in Refactorings

Gheyi, Rohit; Massoni, Tiago; Borba, Paulo

doi:10.1016/j.entcs.2007.03.023

Cited by 1 publication

(1 citation statement)

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“…Their job consists of performing transformations for Alloy through the UML and OCL specifications, and to translate them back to UML and OCL, thus allowing to verify and validate the results of a transformation in Alloy. Gheyi et al (2007) present a catalog of primitive transformations to predict whether a change in the source model will cause semantic loss. To do so, the authors formalize a static semantics for Alloy to match the source model semantics.…”

Section: Figurementioning

confidence: 99%

Preserving conceptual model semantics in the forward engineering of relational schemas

Guidoni¹,

Almeida²,

Guizzardi³

2022

Front. Comput. Sci.

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Forward engineering relational schemas based on conceptual models (in languages such as UML and ER) is an established practice, with several automated transformation approaches discussed in the literature and implemented in production tools. These transformations must bridge the gap between the primitives offered by conceptual modeling languages on the one hand and the relational model on the other. As a result, it is often the case that some of the semantics of the source conceptual model is lost in the transformation process. In this paper, we address this problem by forward engineering additional constraints along with the transformed schema (ultimately implemented as triggers). We formulate our approach in terms of the operations of “flattening” and “lifting” of classes to make our approach largely independent of the particular transformation strategy (one table per hierarchy, one table per class, one table per concrete class, one table per leaf class, etc.). An automated transformation tool is provided that traces the cumulative consequences of the operations as they are applied throughout the transformation process. We report on tests of this tool using models published in an open model repository.

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Section: Figurementioning

confidence: 99%