Title: Efficiently Manifesting Asynchronous Programming Errors in Android Apps,
Abstract: Android, the #1 mobile app framework, enforces the single-GUI-thread model,
in which a single UI thread manages GUI rendering and event dispatching. Due to
this model, it is vital to avoid blocking the UI thread for responsiveness. One
common practice is to offload long-running tasks into async threads. To achieve
this, Android provides various async programming constructs, and leaves
developers themselves to obey the rules implied by the model. However, as our
study reveals, more than 25% apps violate these rules and introduce
hard-to-detect, fail-stop errors, which we term as aysnc programming errors
(APEs). To this end, this paper introduces APEChecker, a technique to
automatically and efficiently manifest APEs. The key idea is to characterize
APEs as specific fault patterns, and synergistically combine static analysis
and dynamic UI exploration to detect and verify such errors. Among the 40
real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51
are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs
than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces
testing time from half an hour to a few minutes. On a specific type of APEs,
APEChecker confirms 5X more errors than the data race detection tool,
EventRacer, with very few false alarms.