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http://dx.doi.org/10.15207/JKCS.2022.13.03.001

A Study on Automatic Classification of Class Diagram Images  

Kim, Dong Kwan (Department of Computer Engineering, Mokpo National Maritime University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.3, 2022 , pp. 1-9 More about this Journal
Abstract
UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.
Keywords
Software convergence; Unified Modeling Language; Object-oriented modeling; Class diagram; Deep learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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