Automated GUI widgets classification  

作　　者：Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING 

机构地区：[1]School of Computer Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China [2]School of Computer Science and Engineering,Nanyang Technological University,Singap

出　　处：《Frontiers of Computer Science》2023年第1期233-235,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Nature Science Foundation of China(Grant Nos.61972359,62132014);the Zhejiang Provincial Natural Science Foundation of China(LY19F020052);Zhejiang Provincial Key Research and Development Program of China(2022C01045).

摘　　要：1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these tasks.Researchers in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific type.The authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI widgets.Chen et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关 键 词：GUI USER classify 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]