Automated cross-platform inconsistency detection for mobile apps

M. Fazzini, A. Orso
{"title":"Automated cross-platform inconsistency detection for mobile apps","authors":"M. Fazzini, A. Orso","doi":"10.1109/ASE.2017.8115644","DOIUrl":null,"url":null,"abstract":"Testing of Android apps is particularly challenging due to the fragmentation of the Android ecosystem in terms of both devices and operating system versions. Developers must in fact ensure not only that their apps behave as expected, but also that the apps' behavior is consistent across platforms. To support this task, we propose DiffDroid, a new technique that helps developers automatically find cross-platform inconsistencies (CPIs) in mobile apps. DiffDroid combines input generation and differential testing to compare the behavior of an app on different platforms and identify possible inconsistencies. Given an app, DiffDroid (1) generates test inputs for the app, (2) runs the app with these inputs on a reference device and builds a model of the app behavior, (3) runs the app with the same inputs on a set of other devices, and (4) compares the behavior of the app on these different devices with the model of its behavior on the reference device. We implemented DiFFDRoiD and performed an evaluation of our approach on 5 benchmarks and over 130 platforms. our results show that DiFFDRoiD can identify CPis on real apps efficiently and with a limited number of false positives. DiFFDRoiD and our experimental infrastructure are publicly available.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASE.2017.8115644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 45

Abstract

Testing of Android apps is particularly challenging due to the fragmentation of the Android ecosystem in terms of both devices and operating system versions. Developers must in fact ensure not only that their apps behave as expected, but also that the apps' behavior is consistent across platforms. To support this task, we propose DiffDroid, a new technique that helps developers automatically find cross-platform inconsistencies (CPIs) in mobile apps. DiffDroid combines input generation and differential testing to compare the behavior of an app on different platforms and identify possible inconsistencies. Given an app, DiffDroid (1) generates test inputs for the app, (2) runs the app with these inputs on a reference device and builds a model of the app behavior, (3) runs the app with the same inputs on a set of other devices, and (4) compares the behavior of the app on these different devices with the model of its behavior on the reference device. We implemented DiFFDRoiD and performed an evaluation of our approach on 5 benchmarks and over 130 platforms. our results show that DiFFDRoiD can identify CPis on real apps efficiently and with a limited number of false positives. DiFFDRoiD and our experimental infrastructure are publicly available.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
自动跨平台不一致检测移动应用程序
由于Android生态系统在设备和操作系统版本上的分裂性,测试Android应用尤其具有挑战性。事实上,开发者不仅要确保他们的应用符合预期,还要确保应用在不同平台上的行为是一致的。为了支持这项任务,我们提出了DiffDroid,这是一种新技术,可以帮助开发人员自动发现移动应用程序中的跨平台不一致性(cpi)。DiffDroid结合了输入生成和差异测试来比较应用在不同平台上的行为,并找出可能的不一致之处。给定一个应用程序,DiffDroid(1)为应用程序生成测试输入,(2)在参考设备上使用这些输入运行应用程序并建立应用程序行为模型,(3)在一组其他设备上使用相同的输入运行应用程序,(4)将应用程序在这些不同设备上的行为与其在参考设备上的行为模型进行比较。我们实现了DiFFDRoiD,并在5个基准测试和130多个平台上对我们的方法进行了评估。我们的结果表明,DiFFDRoiD可以有效地识别真实应用程序上的cpi,并且假阳性数量有限。DiFFDRoiD和我们的实验基础设施是公开的。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
TiQi: A natural language interface for querying software project data A comprehensive study on real world concurrency bugs in Node.js Managing software evolution through semantic history slicing Software performance self-adaptation through efficient model predictive control Privacy-aware data-intensive applications
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1