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Same App, Different Behaviors: Uncovering Device-specific Behaviors in Android Apps

  • Zikan Dong
  • , Yanjie Zhao
  • , Tianming Liu
  • , Chao Wang
  • , Guosheng Xu
  • , Guoai Xu*
  • , Lin Zhang*
  • , Haoyu Wang*
  • *Corresponding author for this work
  • Beijing University of Posts and Telecommunications
  • Huazhong University of Science and Technology
  • Monash University
  • The National Computer Emergency Response Team/Coordination Center of China (CNCERT/CC)

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Android ecosystem is significantly challenged by fragmentation, arising from diverse system versions, device specifications, and manufacturer customizations. The growing divergence among devices leads to marked variations in how a given app behaves across diverse devices. This is referred to as device-specific behaviors. Fragmentation not only complicates development processes but also impacts the overall industry by increasing maintenance costs and potentially harming user experience due to inconsistent app performance. In this work, we present the first large-scale empirical study of device-specific behaviors in real-world Android apps. We have designed a three-phase static analysis framework to accurately detect and understand the device-specific behaviors. Upon employing our tool on a dataset comprising more than 20,000 apps, we detected device-specific behaviors in 2,357 of them. By examining the distribution of device-specific behaviors, our analysis revealed that apps within the Chinese third-party app market exhibit more such behaviors compared to their counterparts in Google Play. Additionally, these behaviors are more likely to feature dominant brands that hold larger market shares. Reflecting this, we have classified these device-specific behaviors into 29 categories based on the functionalities implemented, providing a structured insight that is crucial for developers and stakeholders in the industry. Beyond the common behaviors, such as issue fixes and feature adaptations, we have observed 33 aggressive apps, including popular ones with millions of downloads. These apps abuse system properties of customized ROMs to obtain user-unresettable identifiers without requiring any permissions, posing significant privacy risks. Finally, we investigated the origins of device-specific behaviors, highlighting the significant challenges developers encounter in implementing them comprehensively. Our research aims to inform and equip industry practitioners with knowledge to enhance user experience and user privacy, marking a critical step toward addressing the less touched yet vital aspect of device-specific behaviors in the Android ecosystem.

Original languageEnglish
Title of host publicationProceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
PublisherAssociation for Computing Machinery, Inc
Pages2099-2109
Number of pages11
ISBN (Electronic)9798400712487
DOIs
StatePublished - 27 Oct 2024
Externally publishedYes
Event39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024 - Sacramento, United States
Duration: 28 Oct 20241 Nov 2024

Publication series

NameProceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024

Conference

Conference39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
Country/TerritoryUnited States
CitySacramento
Period28/10/241/11/24

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