Guided, stochastic model-based GUI testing of Android apps
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.
BibTeX
@inproceedings{Su-al:FSE17,
author = {Ting Su and
Guozhu Meng and
Yuting Chen and
Ke Wu and
Weiming Yang and
Yao Yao and
Geguang Pu and
Yang Liu and
Zhendong Su},
title = {Guided, stochastic model-based {GUI} testing of Android apps},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {245--256},
publisher = {{ACM}},
year = {2017},
}