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Open AccessArticle

Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing

1
Center of Information Systems Design Technologies, Kaunas University of Technology, 51368 Kaunas, Lithuania
2
Department of Information Systems, Kaunas University of Technology, Studentu str. 50-309, 51368 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6835; https://doi.org/10.3390/app10196835
Received: 10 August 2020 / Revised: 16 September 2020 / Accepted: 25 September 2020 / Published: 29 September 2020
Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development. View Full-Text
Keywords: model-to-model transformation; M2M transformation; model-driven development of M2M transformation; natural language processing; NLP; UML profile; CASE tool model-to-model transformation; M2M transformation; model-driven development of M2M transformation; natural language processing; NLP; UML profile; CASE tool
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MDPI and ACS Style

Danenas, P.; Skersys, T.; Butleris, R. Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing. Appl. Sci. 2020, 10, 6835. https://doi.org/10.3390/app10196835

AMA Style

Danenas P, Skersys T, Butleris R. Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing. Applied Sciences. 2020; 10(19):6835. https://doi.org/10.3390/app10196835

Chicago/Turabian Style

Danenas, Paulius; Skersys, Tomas; Butleris, Rimantas. 2020. "Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing" Appl. Sci. 10, no. 19: 6835. https://doi.org/10.3390/app10196835

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