Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations. (June 2015)

• Record Type: Journal Article
• Authors: Avazpour, Iman; Grundy, John; Grunske, Lars
• Is Part Of: Journal of visual languages & computing. Volume 28(2015)
• Page Start: 195
• Page End: 211
• Publication Date: 2015-06
• Subjects: Model driven engineering -- Model transformation -- Visual notation -- Recommender system -- Concrete visualizations; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2015.02.005
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: 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. Abstract : Author-Highlights: We provide direct manipulation of visual notations for model transformation tasks. A recommender system is used to suggest source and target model correspondences. We provide a tool, CONVErT, to realize the approach. User study shows acceptance of concrete visual approach for model transformation.