ACAT: A Novel Machine-Learning-Based Tool for Automating Android Application Testing

@inproceedings{Rosenfeld2017ACATAN,
  title={ACAT: A Novel Machine-Learning-Based Tool for Automating Android Application Testing},
  author={Ariel Rosenfeld and Odaya Kardashov and Orel Zang},
  booktitle={Haifa Verification Conference},
  year={2017}
}
Mobile applications are being used every day by more than half of the world’s population to perform a great variety of tasks. With the increasingly widespread usage of these applications, the need arises for efficient techniques to test them. Many frameworks allow automating the process of application testing, however existing frameworks mainly rely on the application developer for providing testing scripts for each developed application, thus preventing reuse of these tests for similar… 
Automation of Android Applications Functional Testing Using Machine Learning Activities Classification
TLDR
A novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques and reusing generic test scenarios to relieve some of the manual functional testing burden by automatically classifying each of the application's screens to a set of common screen behaviors.
Speedroid: A Novel Automation Testing Tool for Mobile Apps
TLDR
The study shows that Speedriod will result as a pillar of digital transformation by providing various features like integrity, usability, efficiency and compatible to both iOS and Android, ready to use in regression testing, reporting, logging and minimal tool learning efforts without any knowledge of programming language and contributing scripts.

References

SHOWING 1-4 OF 4 REFERENCES
Automation of Android Applications Testing Using Machine Learning Activities Classification
TLDR
A novel approach for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios is presented and it is shown that the developed testing tool, based on the proposed approach, outperforms standard methods in realistic settings.
Automation of Android Applications Functional Testing Using Machine Learning Activities Classification
TLDR
A novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques and reusing generic test scenarios to relieve some of the manual functional testing burden by automatically classifying each of the application's screens to a set of common screen behaviors.
Automated Test Input Generation for Android: Are We There Yet? (E)
TLDR
A thorough comparison of the main existing test input generation tools for Android is performed, evaluating the effectiveness of these tools, and their corresponding techniques, according to four metrics: ease of use, ability to work on multiple platforms, code coverage, and ability to detect faults.
Mobile Application Testing: A Tutorial
To cope with frequent upgrades of mobile devices and technologies, engineers need a reusable and cost-effective environment for testing mobile applications and an elastic infrastructure to support