Efficiently Manifesting Asynchronous Programming Errors in Android Apps

TL;DR

This paper presents APEChecker, a novel tool that combines static and dynamic analysis to efficiently detect and verify asynchronous programming errors in Android apps, significantly outperforming existing testing tools.

Contribution

Introducing APEChecker, a new approach that characterizes and detects asynchronous programming errors in Android apps using combined static and dynamic analysis techniques.

Findings

APEChecker detects 3X more APEs than existing tools.

Reduces testing time from half an hour to a few minutes.

Confirms 5X more errors than data race detection tools.

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.