Automated cross-platform inconsistency detection for mobile apps

Mattia Fazzini, Alessandro Orso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering
EditorsTien N. Nguyen, Grigore Rosu, Massimiliano Di Penta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages308-318
Number of pages11
ISBN (Electronic)9781538626849
DOIs
StatePublished - Nov 20 2017
Externally publishedYes
Event32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017 - Urbana-Champaign, United States
Duration: Oct 30 2017Nov 3 2017

Publication series

NameASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering

Other

Other32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017
Country/TerritoryUnited States
CityUrbana-Champaign
Period10/30/1711/3/17

Bibliographical note

Funding Information:
This work was partially supported by the National Science Foundation under grants CCF-1320783 and CCF-1161821, and by funding from Amazon under the AWS Cloud Credits for Research program, Google, IBM Research, and Microsoft Research

Publisher Copyright:
© 2017 IEEE.

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