Guided gui testing of android apps with minimal restart and approximate learning

Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thou-sands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most pop-ular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called Swift-Hand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is of-ten computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly avail-able tool for Android apps written in Java. Our experimental results show that we can achieve significantly better cover-age than traditional random testing and L∗-based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L∗-based testing