Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations

Avazpour, Iman, Grundy, John and Grunske, Lars 2015, Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations, Journal of visual languages and computing, vol. 28, pp. 195-211, doi: 10.1016/j.jvlc.2015.02.005.

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Title Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations
Author(s) Avazpour, ImanORCID iD for Avazpour, Iman orcid.org/0000-0002-0770-4751
Grundy, JohnORCID iD for Grundy, John orcid.org/0000-0003-4928-7076
Grunske, Lars
Journal name Journal of visual languages and computing
Volume number 28
Start page 195
End page 211
Total pages 17
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-06
ISSN 1045-926X
Keyword(s) Science & Technology
Computer Science, Software Engineering
Computer Science
Model driven engineering
Model transformation
Visual notation
Recommender system
Concrete visualizations
Summary Model transformations are a crucial part of Model-Driven Engineering (MDE) technologies but are usually hard to specify and maintain for many engineers. Most current approaches use meta-model-driven transformation specification via textual scripting languages. These are often hard to specify, understand and maintain. We present a novel approach that instead allows domain experts to discover and specify transformation correspondences using concrete visualizations of example source and target models. From these example model correspondences, complex model transformation implementations are automatically generated. We also introduce a recommender system that helps domain experts and novice users find possible correspondences between large source and target model visualization elements. Correspondences are then specified by directly interacting with suggested recommendations or drag and drop of visual notational elements of source and target visualizations. We have implemented this approach in our prototype tool-set, CONVErT, and applied it to a variety of model transformation examples. Our evaluation of this approach includes a detailed user study of our tool and a quantitative analysis of the recommender system.
Language eng
DOI 10.1016/j.jvlc.2015.02.005
Field of Research 080309 Software Engineering
1702 Cognitive Science
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081632

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