DinoDroid: Testing Android Apps Using Deep Q-Networks

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Yu Zhao, Brent Harrison, Tingting Yu
{"title":"DinoDroid: Testing Android Apps Using Deep Q-Networks","authors":"Yu Zhao, Brent Harrison, Tingting Yu","doi":"10.1145/3652150","DOIUrl":null,"url":null,"abstract":"<p>The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this paper, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.</p>","PeriodicalId":50933,"journal":{"name":"ACM Transactions on Software Engineering and Methodology","volume":"25 1","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Software Engineering and Methodology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3652150","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this paper, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.

DinoDroid:使用深度 Q 网络测试 Android 应用程序
移动设备的巨大需求引发了人们对移动应用程序(App)质量的极大关注。开发人员需要在移动应用程序投放市场前保证其质量。目前已有许多方法使用不同的策略来测试移动应用程序的图形用户界面。然而,由于效果有限,这些方法仍需改进。在本文中,我们提出了一种基于深度 Q 网络的方法 DinoDroid,用于自动测试安卓应用程序。DinoDroid 可从一组现有应用程序中学习行为模型,学习到的模型可用于探索和生成新应用程序的测试。DinoDroid 能够捕捉图形用户界面事件的细粒度细节(如图形用户界面部件的内容),并将其作为特征输入深度神经网络,而深度神经网络则作为代理引导应用程序的探索。DinoDroid 可在探索过程中自动调整所学模型,而无需任何建模策略或预定义规则。我们在 64 个开源安卓应用程序上进行了实验。结果表明,DinoDroid 在代码覆盖率和错误检测方面优于现有的安卓测试工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
ACM Transactions on Software Engineering and Methodology
ACM Transactions on Software Engineering and Methodology 工程技术-计算机:软件工程
CiteScore
6.30
自引率
4.50%
发文量
164
审稿时长
>12 weeks
期刊介绍: Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信