Pivot: learning API-device correlations to facilitate Android compatibility issue detection
Proceedings of the 41st International Conference on Software Engineering(2019)
摘要
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.
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关键词
Android fragmentation, compatibility, learning, static analysis
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