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

Paulius Danenas,Tomas Skersys,Rimantas Butleris

doi:10.3390/app10196835

Paulius Danenas, Tomas Skersys + Show 1 more

Open Access

https://doi.org/10.3390/app10196835

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Journal: Applied sciences	Publication Date: Sep 29, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Kaunas University of Technology

Abstract

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.

Highlights

With the introduction of model driven architecture (MDA) by the object management group (OMG) in 2003, model-to-model (M2M) transformations have become an essential part of the modernAppl
The “Number of executed transformations column” shows the number of transformations performed during the experiment, while the “Number of executed atomic transformations” column presents the actual number of operations performed; the “Number of expected output elements” column shows the total number of elements (ITMCT ) per model acquired after manual extraction, and this is considered as the benchmark result; the “Number of output elements” columns for the original and natural language processing (NLP)-enhanced solution sections indicate the total number of elements that were acquired after transformation processing of each model
We considered that cases containing a single verb phrase and multiple noun phrases could be processed to generate multiple ITMCT output elements (e.g., “assign manager and assistant” can be processed as “assign manager” and “assign assistant”)

Summary

Introduction

With the introduction of model driven architecture (MDA) by the object management group (OMG) in 2003, model-to-model (M2M) transformations have become an essential part of the modernAppl. With the introduction of model driven architecture (MDA) by the object management group (OMG) in 2003, model-to-model (M2M) transformations have become an essential part of the modern. Sci. 2020, 10, 6835 model-driven development paradigm. An additional impulse was given by the introduction of unified modeling language (UML) 2.0, featuring a powerful model extension mechanism. With the adoption of UML 2.0, M2M transformations have become a common means of generating platform-specific models from platform-independent models, which in turn made M2M transformations a highly desirable feature in any advanced computer-aided software engineering (CASE) tool supporting MDA principles. M2M transformations have become a part of common practice in other relevant fields of system engineering, such as model-based systems engineering (MBSE)

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