EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 412 (2023) E3S Web Conf., 412 (2023) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 412, 2023
International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
Article Number 01002
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202341201002
Published online 17 August 2023
  1. Mukhtar, M. I., & Galadanci, B. S. (2018). Automatic code generation from UML diagrams: the state-of-the-art. Science World Journal, 13(4), 47-60. [Google Scholar]
  2. Baidada, C., El Mahi, B., & Jakimi, A. Towards the reverse engineering of UML sequence diagrams for multithreaded java software. [Google Scholar]
  3. Aabidi, M. H., El Mahi, B., Baidada, C., Jakimi, A., & Ammar, H. (2017). Benefits of reverse engineering technologies in software development makerspace. In ITM Web of Conferences (Vol. 13, p. 01028). EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
  4. Singh, K. (2020). Transformation of source code into UML diagrams through visualization tool. International Journal of Advanced Science and Technology, 29(8), 4861-1114. [Google Scholar]
  5. Gosala, B.; Chowdhuri, S.R.; Singh, J.; Gupta, M.; Mishra, A. Automatic, Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network. Appl. Sci. 2021, 11, 4267. [CrossRef] [Google Scholar]
  6. Osman, M. H., Ho-Quang, T., & Chaudron, M. (2018, August). An automated approach for classifying reverse-engineered and forward-engineered UML class diagrams. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 396-399). IEEE. [Google Scholar]
  7. Mangaroliya, K., & Patel, H. (2020). Classification of reverse-engineered class diagram and forward-engineered class diagram using machine learning. arXiv preprint arXiv:2011.07313. [Google Scholar]
  8. Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2018). Code2vec: learning distributed representations of code. CoRR. arXiv preprint arXiv:1803.09473. [Google Scholar]
  9. Jing, D., Yang, H., & Hakeem, H. (2014, September). Using abstraction in MDA-based reverse engineering for creative evolution. In 2014 20th International Conference on Automation and Computing (pp. 67-72). IEEE. [Google Scholar]
  10. Sabir, U., Azam, F., & Anwar, M. W. (2017, December). A comprehensive investigation of model-driven architecture (MDA) for reverse engineering In Proceedings of the 2017 International Conference on Software and e Business (pp. 43-48). [Google Scholar]
  11. Aabidi, M. H., El Mahi, B., Baidada, C., Jakimi, A., & Ammar, H. (2017). Benefits of reverse engineering technologies in software development makerspace. In ITM Web of Conferences (Vol. 13, p. 01028). EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
  12. https://www.simplilearn.com/what-is-graph-neural-network-article [Google Scholar]
  13. Kehagias, D., Jankovic, M., Siavvas, M., & Gelenbe, E. (2021). Investigating the interaction between energy consumption, quality of service, reliability, security, and maintainability of computer systems and networks. SN Computer Science, 2(1), 23. [CrossRef] [PubMed] [Google Scholar]
  14. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 5781. [CrossRef] [Google Scholar]
  15. Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121. [Google Scholar]
  16. Andre, P., Guerin, A., Rozen, A., & Gicquel, A. Raffinement de protocoles de communication par transformation de modèle. [Google Scholar]
  17. Bergström, G., Hujainah, F., Ho-Quang, T., Jolak, R., Rukmono, S. A., Nurwidyantoro, A., & Chaudron, M. R. (2022). Evaluating the layout quality of UML class diagrams using machine learning. Journal of Systems and Software, 192, 111413. [CrossRef] [Google Scholar]
  18. Stikkolorum, D. R., van der Putten, P., Sperandio, C., & Chaudron, M. (2019). Towards Automated Grading of UML Class Diagrams with Machine Learning. BNAIC/BENELEARN, 2491. [Google Scholar]
  19. Ciccozzi, F., Malavolta, I., & Selic, B. (2019). Execution of UML models: a systematic review of research and practice. Software & Systems Modeling, 18, 2313-2360. [CrossRef] [Google Scholar]
  20. Abdelnabi, E. A., Maatuk, A. M., & Hagal, M. (2021, May). Generating uml class diagram from natural language requirements: A survey of approaches and techniques. In 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MISTA (pp. 288-293). IEEE. [Google Scholar]
  21. Abdelnabi, E. A., Maatuk, A. M., Abdelaziz, T. M., & Elakeili, S. M. (2020, December). Generating UML class diagram using NLP techniques and heuristic rules. In 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 277-282). IEEE. [Google Scholar]
  22. Ciancarini, P., Ergasheva, S., Kholmatova, Z., Kruglov, A., Succi, G., Vasquez, X., & Zuev, E. (2020). Analysis of energy consumption of software development process entities. Electronics, 9(10), 1678. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.