DroidbotX: Test case generation tool for android applications using Q-Learning
Android applications provide benefits to mobile phone users by offering operative func-tionalities and interactive user interfaces. However, application crashes give users an unsatisfac-tory experience, and negatively impact the application’s overall rating. Android application crashes can be avoide...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
MDPI
2021
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/25905/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Android applications provide benefits to mobile phone users by offering operative func-tionalities and interactive user interfaces. However, application crashes give users an unsatisfac-tory experience, and negatively impact the application’s overall rating. Android application crashes can be avoided through intensive and extensive testing. In the related literature, the graphical user interface (GUI) test generation tools focus on generating tests and exploring application functions using different approaches. Such tools must choose not only which user interface element to interact with, but also which type of action to be performed, in order to increase the percentage of code coverage and to detect faults with a limited time budget. However, a common limitation in the tools is the low code coverage because of their inability to find the right combination of actions that can drive the application into new and important states. A Q-Learning-based test coverage approach developed in DroidbotX was proposed to generate GUI test cases for Android applications to maximize instruction coverage, method coverage, and activity coverage. The overall performance of the proposed solution was compared to five state-of-the-art test generation tools on 30 Android applications. The DroidbotX test coverage approach achieved 51.5% accuracy for instruction coverage, 57% for method coverage, and 86.5% for activity coverage. It triggered 18 crashes within the time limit and shortest event sequence length compared to the other tools. The results demonstrated that the adaptation of Q-Learning with upper confidence bound (UCB) exploration outperforms other existing state-of-the-art solutions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
|---|