· 2017
Guided, stochastic model-based GUI testing of Android apps
- 368citations
- 2017year
Short summary
Stoat, a novel guided approach, uses dynamic and static analysis to reverse engineer Android app GUI models and then employs Gibbs sampling to mutate these models for diverse test generation, achieving high code and model coverage.
AI-generated from the title and abstract; the full text is not read.
Key points
- Stoat uses dynamic and static analysis to create a stochastic model of an Android app's GUI interactions.
- Gibbs sampling is adapted to iteratively mutate and refine the stochastic model.
- Test generation is guided by mutated models to achieve high code/model coverage and diverse sequences.
- Random system-level event injection further enhances testing effectiveness.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Software
SoftwareComputer Science