Evaluating the Conformity to Types of Unified Modeling Language Diagrams with Feature-Based Neural Networks
| Autor: | Irina-Gabriela Nedelcu, Anca Daniela Ionita |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 14, Iss 20, p 9470 (2024) |
| Druh dokumentu: | article |
| ISSN: | 14209470 2076-3417 |
| DOI: | 10.3390/app14209470 |
| Popis: | This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of modeling elements from class, state machine, and sequence diagrams, enhancing the ability to recognize a larger variety of categories selected for this research. The neural network trained with these features representing parts of the UML concrete syntax demonstrates 90% in classification accuracy on average, in respect to our previous research on UML diagrams classification without using a feature-based dataset. This study concludes that a feature-based approach, combined with advanced neural network architectures, can improve the classification of such images, especially in edge cases where diagrams contain similar graphical details but the whole does not represent a UML diagram. For the given research, we obtained a 0.87 F1 score. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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