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.