Paper Info
Title
Pivot: learning API-device correlations to facilitate Android compatibility issue detection
Abstract
The heavily fragmented Android ecosystem has induced various compatibility issues in Android apps. The search space for such fragmentation-induced compatibility issues (FIC issues) is huge, comprising three dimensions: device models, Android OS versions, and Android APIs. FIC issues, especially those arising from device models, evolve quickly with the frequent release of new device models to the market. As a result, an automated technique is desired to maintain timely knowledge of such FIC issues, which are mostly undocumented. In this paper, we propose such a technique, PIVOT, that automatically learns API-device correlations of FIC issues from existing Android apps. PIVOT extracts and prioritizes API-device correlations from a given corpus of Android apps. We evaluated PIVOT with popular Android apps on Google Play. Evaluation results show that PIVOT can effectively prioritize valid API-device correlations for app corpora collected at different time. Leveraging the knowledge in the learned API-device correlations, we further conducted a case study and successfully uncovered ten previously-undetected FIC issues in open-source Android apps.
Year
DOI
Venue
2019
10.1109/ICSE.2019.00094
Proceedings of the 41st International Conference on Software Engineering
Keywords
Field
DocType
Android fragmentation, compatibility, learning, static analysis
Automated technique,Android (operating system),Compatibility (mechanics),Computer science,Static analysis,Real-time computing,Human–computer interaction
Conference
ISSN
ISBN
Citations 
0270-5257
978-1-7281-0870-4
6
PageRank 
References 
Authors
0.48
19
3
Name
Order
Citations
PageRank
Lili Wei1916.51
Yepang Liu241524.58
S. C. Cheung32657162.89