An Approach to GUI Test Scenario Generation Using Machine Learning

J. Gao, ShiTing Li, Chuanqi Tao, Yejun He, Amrutha Pavani Anumalasetty, Erica Wilson Joseph, Akshata Hatwar Kumbashi Sripathi, Himabindu Nayani
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Abstract

With the fast advance of artificial intelligence technology and data-driven machine learning techniques, more and more AI approaches are applied in software engineering activities, such as coding, testing and etc.. Conventionally, test engineers use manual testing tools to test mobile apps and deliver products. Object detection technology like YOLO is widely used in image processing these days. Inspired from this, on the basis of detecting GUI elements using machine learning models, we propose an automated approach to GUI test scenario generation based on mockup diagrams. The list of possible scenarios can be visualized using NetworkX which can indicate the feasibility and effectiveness of the proposed approach.
一种使用机器学习生成GUI测试场景的方法
随着人工智能技术和数据驱动的机器学习技术的快速发展,越来越多的人工智能方法被应用到软件工程活动中,如编码、测试等。传统上,测试工程师使用手动测试工具来测试移动应用程序并交付产品。像YOLO这样的目标检测技术目前在图像处理中得到了广泛的应用。受此启发,在使用机器学习模型检测GUI元素的基础上,我们提出了一种基于模型图自动生成GUI测试场景的方法。可以使用NetworkX将可能的场景列表可视化,这可以表明所提出方法的可行性和有效性。
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