Automation of Android applications functional testing using machine learning activities classification

Following the ever-growing demand for mobile applications, researchers are constantly developing new test automation solutions for mobile developers. However, researchers have yet to produce an automated functional testing approach, resulting in many developers relying on a resource consuming manual testing. In this paper, we present a novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques and reusing generic test scenarios. Our approach aims at relieving some of the manual functional testing burden by automatically classifying each of the application's screens to a set of common screen behaviors for which generic test scripts can be instantiated and reused. We empirically demonstrate the potential benefits of our approach in two experiments: First, using 26 randomly selected Android applications, we show that our approach can successfully instantiate and reuse generic functional tests and discover functional bugs. Second, in a human study with two experienced human mobile testers, we show that our approach can automatically cover a large portion of the human testers' work suggesting a significant potential relief in the manual testing efforts.