A Framework for In-Vivo Testing of Mobile Applications

The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations. In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high- vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device.Comment: Research paper accepted to ICST'20, 10+1 page

Automated Functional Fuzzing of Android Apps

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Besides fail-stop errors like app crashes, functional correctness is critical for an app's success. However, functional bugs (e.g., inadvertent function failures, sudden user data lost, and incorrect display information) are prevalent, even in popular, well-tested apps, due to significant challenges in effectively detecting them: (1) current practices heavily rely on expensive, small-scale manual validation (the lack of automation); and (2) modern automated testing has been limited to app crashes (the lack of test oracles). This paper fills this gap by introducing independent view fuzzing, the first automated approach for detecting functional bugs in Android apps. Our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs. We have realized our approach as a practical, end-to-end functional fuzzing tool, Genie. Given an off-the-shelf app, Genie (1) automatically detects functional bugs without requiring human-provided tests and oracles (thus fully automated), and (2) the detected functional bugs are diverse (thus general and not limited to specific functional properties). We have evaluated Genie on twelve popular, well-maintained Android apps and successfully uncovered 33 previously unknown functional bugs in their latest releases -- all have been confirmed, and 21 have already been fixed. Most of the detected bugs are nontrivial and have escaped developer (and user) testing for at least one year and affected many app releases, thus clearly demonstrating Genie's practical utility