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Android applications (apps) grow dramatically in recent years. Apps are userinterface (UI) centric typically. Rapid UI responsiveness is key considerationto app developers. However, we still lack a handy tool for profiling appperformance so as to diagnose performance problems. This paper presentsPersisDroid, a tool specifically designed for this task. The key notion ofPersisDroid is that the UI-triggered asynchronous executions also contribute tothe UI performance, and hence its performance should be properly captured tofacilitate performance diagnosis. However, Android allows tremendous ways tostart the asynchronous executions, posing a great challenge to profiling suchexecution. This paper finds that they can be grouped into six categories. As aresult, they can be tracked and profiled according to the specifics of eachcategory with a dynamic instrumentation approach carefully tailored forAndroid. PersisDroid can then properly profile the asynchronous executions intask granularity, which equips it with low-overhead and high compatibilitymerits. Most importantly, the profiling data can greatly help the developers indetecting and locating performance anomalies. We code and open-source releasePersisDroid. The tool is applied in diagnosing 20 open-source apps, and we find11 of them contain potential performance problems, which shows itseffectiveness in performance diagnosis for Android apps.

PersisDroid: Android Performance Diagnosis via Anatomizing Asynchronous Executions