Automatic UML Defects Detection based on Image of Diagram

Murielle Lokonon, Vinasetan Houndji

2022

Abstract

Unified Modeling Language (UML) is a standardized modeling language used to design software systems. However, software engineering learners often have difficulties understanding UML and often repeat the same mistakes. Several solutions automatically correct UML diagrams. These solutions are generally restricted to the modeling tool used or need teachers’ intervention for providing exercises, answers, and other rules to consider for diagrams corrections. This paper proposes a tool that allows the automatic correction of UML diagrams by taking an image as input. The aim is to help UML practicers get automatic feedback on their diagrams regardless of how they have represented them. We have conducted our experiments on the use case diagrams. We have first built a dataset of images of the most elements encountered in the use case diagrams. Then, based on this dataset, we have trained some machine learning models using the Detectron2 library developed by Facebook AI Research (FAIR). Finally, we have used the model with the best performances and a predefined list of errors to set up a tool that can syntactically correct any use case diagram with relatively good precision. Thanks to its genericity, the use of this tool is easier and more practical than the state-of-the-art UML diagrams correction systems.

Paper Citation

in Harvard Style

Lokonon M. and Houndji V. (2022). Automatic UML Defects Detection based on Image of Diagram. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 193-198. DOI: 10.5220/0011316900003277

in Bibtex Style

@conference{delta22,
author={Murielle Lokonon and Vinasetan Houndji},
title={Automatic UML Defects Detection based on Image of Diagram},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={193-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011316900003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Automatic UML Defects Detection based on Image of Diagram
SN - 978-989-758-584-5
AU - Lokonon M.
AU - Houndji V.
PY - 2022
SP - 193
EP - 198
DO - 10.5220/0011316900003277