Guided, stochastic model-based GUI testing of Android apps

TLDR

Mobile apps are ubiquitous and developed under tight time‑to‑market pressure, making correctness and reliability a critical challenge. The paper introduces Stoat, a guided stochastic model‑based testing approach for Android apps. Stoat first reverse‑engineers a stochastic GUI model using dynamic and static analysis, then iteratively refines it with Gibbs sampling to guide test generation toward high coverage and diverse sequences, while injecting random system events during testing.

Abstract

Mobile apps are ubiquitous, operate in complex environments and are developed under the time-to-market pressure. Ensuring their correctness and reliability thus becomes an important challenge. This paper introduces Stoat, a novel guided approach to perform stochastic model-based testing on Android apps. Stoat operates in two phases: (1) Given an app as input, it uses dynamic analysis enhanced by a weighted UI exploration strategy and static analysis to reverse engineer a stochastic model of the app's GUI interactions; and (2) it adapts Gibbs sampling to iteratively mutate/refine the stochastic model and guides test generation from the mutated models toward achieving high code and model coverage and exhibiting diverse sequences. During testing, system-level events are randomly injected to further enhance the testing effectiveness.