Automatic Collaborative Testing of Applications Integrating Text Features and Priority Experience Replay

被引：0

作者：

Cai, Lizhi ^{[1
,2
]}

Wang, Jin ^{[1
,2
]}

Chen, Mingang ^{[2
]}

Wang, Jilong ^{[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 & Evaluat, Shanghai, Peoples R China

来源：

2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY, QRS | 2022年

基金：

国家重点研发计划;

关键词：

Application Testing; Reinforcement Learning; Text Information;

D O I：

10.1109/QRS57517.2022.00020

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

With the popularity of deep reinforcement learning(DRL), people have great interest in using deep reinforcement learning for application automated testing. However, most automated testing methods based on reinforcement learning ignore text information, use random sampling in experience replay and ignore the characteristics of Android automated testing. To solve above problem, this paper proposes ITPRTesting(Integrated Text feature information and Priority experience in Testing). It extracts the text information in the interface and uses the BERT algorithm to generate sentence vectors. It fuses the interactive control feature diagram(ICFD), which is mentioned in the previous work, and text information as the state required by reinforcement learning. And in reinforcement learning, the priority experience replay is combined, also the traditional priority experience replay is improved. This paper has carried out experiments on 10 open source applications. The experimental results show that ITPRTesting is superior to other methods in statement coverage and branch coverage.

引用

页码：95 / 104

页数：10