Authors:
Karima Berramla
1
;
2
;
El Abbassia Deba
2
;
Jiechen Wu
3
;
Houari Sahraoui
3
and
Abou El Hassen Benyamina
2
Affiliations:
1
Ain Temouchent University Center, Algeria
;
2
LAPECI Laboratory, Oran 1 University Ahmed Ben Bella, Algeria
;
3
DIRO, Université de Montréal, Canada
Keyword(s):
Model Transformation, Model Transformation by Example, Learning System, SMT System, IBM1 Model.
Abstract:
In the last decade, Model-Driven Engineering (MDE) has experienced rapid growth in the software development community. In this context, model transformation occupies an important place that automates the transitions between development steps during the application production. To implement this transformation process, we require mastering languages and tools, but more importantly the semantic equivalence between the involved input and output metamodels. This knowledge is in general difficult to acquire, which makes transformation writing complex, time-consuming, and error-prone. In this paper, we propose a new model transformation by example approach to simplify model transformations, using Statistical Machine Translation (SMT). Our approach exploits the power of SMT by converting models in natural language texts and by processing them using models trained with IBM1 model.