WiFi P2P allows mobile apps to connect to each other via WiFi without an intermediate access point. This communication mode is widely used by mobile apps to support interactions with one or more devices simultaneously. However, testing such P2P apps remains a challenge for app developers as i) existing testing frameworks lack support for WiFi P2P, and ii) WiFi P2P testing fails to scale when considering a deployment on more than two devices. In this paper, we therefore propose an acceptance testing framework, named Androfleet, to automate testing of WiFi P2P mobile apps at scale. Beyond the capability of testing point-to-point interactions under various conditions, An-drofleet supports the deployment and the emulation of a fleet of mobile devices as part of an alpha testing phase in order to assess the robustness of a WiFi P2P app once deployed in the field. To validate Androfleet, we demonstrate the detection of failing black-box acceptance tests for WiFi P2P apps and we capture the conditions under which such a mobile app can correctly work in the field. The demo video of Androfleet is made available from https://youtu.be/gJ5_Ed7XL04.

The reputation of a mobile app vendor is crucial to survive amongst the ever increasing competition. However this reputation largely depends on the quality of the apps, both functional and non-functional. One major non-functional requirement of mobile apps is to guarantee smooth UI interactions, since choppy scrolling or navigation caused by performance problems on a mobile device's limited hardware resources, is highly annoying for end-users. The main research challenge of automatically identifying UI performance problems on mobile devices is that the performance of an app highly varies depending on its context---i.e., the hardware and software configurations on which it runs.

App stores are digital distribution platforms that put available apps that run on mobile devices. Current stores are software repositories that deliver apps upon user requests. However, when an app has a bug, the store continues delivering defective apps until the developer uploads a fixed version, thus impacting on the reputation of both store and app developer. In this paper, we envision a new generation of app stores that: (a) reduce human intervention to maintain mobile apps; and (b) enhance store services with smart and autonomous functionalities to automatically increase the quality of the delivered apps. We sketch a prototype of our envisioned app store and we discuss the functionalities that current stores an enhance by incorporating automatic software repair techniques.