论文标题
使用机器学习的反向工程课程图和前向设计的类图进行分类
Classification of Reverse-Engineered Class Diagram and Forward-Engineered Class Diagram using Machine Learning
论文作者
论文摘要
UML类图对于可视化我们正在处理的整个软件，并通过显示系统类，其属性，方法和与其他对象的关系来帮助理解整个系统非常重要。在现实世界中，有两种类型的类图表工程师可以与1）前向工程图（FWCD）一起手工制作作为前瞻性开发过程的一部分，以及2）。逆向班级图（RECD）是从源代码进行反向工程的那些图。在软件行业中，在使用新的开放软件项目时，重要的是要知道哪种类型的类图。在特定项目中使用了哪个UML图是要知道的重要因素？为了解决此问题，我们建议建立一个可以将UML图分类为FWCD或RECD的分类器。我们建议通过使用监督的机器学习技术来解决此问题。在此方法中，该方法涉及分析用于分类类图的功能。在此过程中使用了不同的机器学习模型，随机森林算法已被证明是最好的。在999个班级图上进行了性能测试。
UML Class diagram is very important to visualize the whole software we are working on and helps understand the whole system in the easiest way possible by showing the system classes, its attributes, methods, and relations with other objects. In the real world, there are two types of Class diagram engineers work with namely 1) Forward Engineered Class Diagram (FwCD) which are hand-made as part of the forward-looking development process, and 2). Reverse Engineered Class Diagram (RECD) which are those diagrams that are reverse engineered from the source code. In the software industry while working with new open software projects it is important to know which type of class diagram it is. Which UML diagram was used in a particular project is an important factor to be known? To solve this problem, we propose to build a classifier that can classify a UML diagram into FwCD or RECD. We propose to solve this problem by using a supervised Machine Learning technique. The approach in this involves analyzing the features that are useful in classifying class diagrams. Different Machine Learning models are used in this process and the Random Forest algorithm has proved to be the best out of all. Performance testing was done on 999 Class diagrams.
