文献类型：会议论文

英文题名：Automated Testing of Android Applications Integrating Residual Network and Deep Reinforcement Learning

作者：Cai, Lizhi[1,2];Wang, Jilong[1,2];Cheng, Mingang[2];Wang, Jin[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Dev Ctr Comp Software Technol, Shanghai Key Lab Comp Software Testing & Evaluati, Shanghai, Peoples R China

会议论文集：21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

会议日期：DEC 06-10, 2021

会议地点：Hainan, PEOPLES R CHINA

语种：英文

外文关键词：Automation; Application Testing; Reinforcement Learning

摘要：With the improvements of Deep Reinforcement Learning (DRL), there have been tremendous interests in utilizing DRL for automated application testing. However, most automated testing methods based on reinforcement learning have the problem of interacting with invalid UI areas and invalid interactions with controls. To solve this problem, this paper extracts the page features, constructs the Interactive Control Feature Diagram(ICCD); improves the DDQN network structure, adds the residual network, makes the algorithm take the picture as the input, and splits the original single output action(n*w*h) into two successive outputs: the interaction(1,n) and the position(1,w*h); a new reward function which combines the interaction times and the image similarity of ICCD is proposed to explore different UIs and ensure that there will be more than one action will be executed under the same UI. Experiments are carried out on five open source applications. The experimental results show that the proposed method is superior to other methods in code coverage and branch coverage.