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arXiv · 1512.07950

PersisDroid: Android Performance Diagnosis via Anatomizing Asynchronous Executions

Yu Kang·Yangfan Zhou·Hui Xu·Michael R. Lyu
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Abstract

Android applications (apps) grow dramatically in recent years. Apps are user interface (UI) centric typically. Rapid UI responsiveness is key consideration to app developers. However, we still lack a handy tool for profiling app performance so as to diagnose performance problems. This paper presents PersisDroid, a tool specifically designed for this task. The key notion of PersisDroid is that the UI-triggered asynchronous executions also contribute to the UI performance, and hence its performance should be properly captured to facilitate performance diagnosis. However, Android allows tremendous ways to start the asynchronous executions, posing a great challenge to profiling such execution. This paper finds that they can be grouped into six categories. As a result, they can be tracked and profiled according to the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. PersisDroid can then properly profile the asynchronous executions in task granularity, which equips it with low-overhead and high compatibility merits. Most importantly, the profiling data can greatly help the developers in detecting and locating performance anomalies. We code and open-source release PersisDroid. The tool is applied in diagnosing 20 open-source apps, and we find 11 of them contain potential performance problems, which shows its effectiveness in performance diagnosis for Android apps.

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Yu Kang, Yangfan Zhou, Hui Xu, Michael R. Lyu. 2015-12-25. PersisDroid: Android Performance Diagnosis via Anatomizing Asynchronous Executions. https://arxiv.org/abs/1512.07950

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