Classification of UML Diagrams to Support Software Engineering Education

J. F. Tavares, Yandre M. G. Costa, T. Colanzi
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引用次数: 3

Abstract

There is a huge necessity for tools that implement accessibility in Software Engineering (SE) education. The use of diagrams to teach software development is a very common practice, and there are a lot of UML diagrams represented as images in didactic materials that need an accessible version for visually impaired or blind students. Machine learning techniques, such as deep learning, can be used to automate this task. The practical application of deep learning in many classification problems in the context of SE is problematic due to the large volumes of labeled data required for training. Transfer learning techniques can help in this type of task by taking advantage of pre-trained models based on Convolutional Neural Networks (CNN), so that better results may be achieved even with few images. In this work, we applied transfer learning and data augmentation for UML diagrams classification on a dataset specially created for the development of this work, containing six types of UML diagrams. The dataset was also made available as a contribution of this work. We experimented three widely-known CNN architectures: VGG16, RestNet50, and InceptionV3. The results demonstrated that the use of transfer learning contributes for achieving good results even using scarce data. However, there is still a room for improvement regarding the successful classification of the UML diagrams addressed in this work.
UML图的分类以支持软件工程教育
在软件工程(SE)教育中,非常需要实现可访问性的工具。使用图来教授软件开发是一种非常常见的做法,并且在教学材料中有许多UML图以图像的形式表示,需要为视障或失明的学生提供一个可访问的版本。机器学习技术,如深度学习,可以用来自动完成这项任务。由于训练需要大量的标记数据,深度学习在SE背景下的许多分类问题中的实际应用是有问题的。迁移学习技术可以通过利用基于卷积神经网络(CNN)的预训练模型来帮助完成这类任务,因此即使使用很少的图像也可以获得更好的结果。在这项工作中,我们在一个专门为这项工作的开发而创建的数据集上应用了迁移学习和数据增强的UML图分类,该数据集包含六种类型的UML图。该数据集也作为这项工作的贡献而提供。我们实验了三种广为人知的CNN架构:VGG16、RestNet50和InceptionV3。结果表明,即使使用稀缺的数据,迁移学习的使用也有助于获得良好的结果。然而,在这项工作中,关于UML图的成功分类仍然有改进的空间。
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