Target Directed Event Sequence Generation for Android Applications
Jiwei Yan, Tianyong Wu, Jun Yan, Jian Zhang

TL;DR
This paper introduces LATTE, a dynamic model for Android app testing that considers GUI and back stack information, enabling targeted event sequence generation to improve coverage and efficiency.
Contribution
The paper presents LATTE, a novel dynamic model incorporating GUI and back stack details, and a target-directed test generation method for Android apps.
Findings
High coverage achieved in experiments on real-world apps.
Effective generation of short event sequences for specific targets.
LATTE reduces state explosion during model construction.
Abstract
Testing is a commonly used approach to ensure the quality of software, of which model-based testing is a hot topic to test GUI programs such as Android applications (apps). Existing approaches mainly either dynamically construct a model that only contains the GUI information, or build a model in the view of code that may fail to describe the changes of GUI widgets during runtime. Besides, most of these models do not support back stack that is a particular mechanism of Android. Therefore, this paper proposes a model LATTE that is constructed dynamically with consideration of the view information in the widgets as well as the back stack, to describe the transition between GUI widgets. We also propose a label set to link the elements of the LATTE model to program snippets. The user can define a subset of the label set as a target for the testing requirements that need to cover some…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Advanced Malware Detection Techniques · Software Reliability and Analysis Research
