When generating GUI tests for Android apps, it typically is a separate test computer that generates interactions, which are then executed on an actual Android device. While this approach is efficient in the sense that apps and interactions execute quickly, the communication overhead between test computer and device slows down testing considerably. In this work, we present DD-2, a test generator for Android that tests other apps on the device using Android accessibility services. In our experiments, DD-2 has shown to be 3.2 times faster than its computer-device counterpart, while sharing the same source code.

Android applications (apps) represent an ever increasing portion of the software market. Automated test input generators are the state of the art for testing and security analysis. We introduce DroidMate-2 (DM-2), a platform to easily assist both developers and researchers to customize, develop and test new test generators. DM-2 can be used without app instrumentation or operating system modifications, as a test generator on real devices and emulators for app testing or regression testing. Additionally, it provides sensitive resource monitoring or blocking capabilities through a lightweight app instrumentation, out-of-thebox statement coverage measurement through a fully-fledged app instrumentation and native experiment reproducibility. In our experiments we compared DM-2 against DroidBot, a state-of-the-art test generator by measuring statement coverage. Our results show that DM-2 reached 96% of its peak coverage in less than 2/3 of the time needed by DroidBot, allowing for better and more efficient tests. On short runs (5 minutes) DM-2 outperformed DroidBot by 7% while in longer runs (1 hour) this difference increases to 8%. ACM DL Artifact: https://www.doi.org/10.1145/3264864

Can we automatically learn how users expect an application to behave? Yes, if we consider an application from the users perspective. Whenever presented with an unfamiliar app, the user not only regards the context presented by this particular application, but rather considers previous experiences from other applications. This research presents an approach to reflect this procedure by automatically learning user expectations from the semantic contexts over multiple applications. Once the user expectations are established, this knowledge can be used as an oracle, to test if an application follows the user's expectations or entails surprising behavior by error or deliberately.