Finding non-crashing functional bugs for Android apps is challenging for both manual testing and automated GUI testing techniques. This paper introduces and designs a general and practical testing technique based on the idea of property-based testing for finding such bugs. Specifically, our technique incorporates (1) a property description language (PDL) to allow specifying desired app properties, and (2) two exploration strategies as the input generators for effectively validating the properties. We implemented our technique as a tool named Kea and evaluated it on 124 historical bugs from eight real-world, popular Android apps. Our evaluation shows that our PDL can specify all the app properties violated by these historical bugs, demonstrating its generability for finding functional bugs. Kea successfully found 66 (68.0%) and 92 (94.8%) of the 97 historical bugs in scope under the two exploration strategies, demonstrating its practicability. Moreover, Kea found 25 new functional bugs on the latest versions of these eight apps, given the specified properties. To date, all these bugs have been confirmed, and 21 have been fixed. In comparison, prior state-of-the-art techniques found only 13 (13.4%) historical bugs and 1 new bug. We have made all the artifacts publicly available at https://github.com/ecnusse/Kea.

Android apps are ubiquitous and serve many aspects of our daily lives. Ensuring their functional correctness is crucial for their success. To date, we still lack a general and in-depth understanding of functional bugs, which hinders the development of practices and techniques to tackle functional bugs. To fill this gap, we conduct the first systematic study on 399 functional bugs from 8 popular open-source and representative Android apps to investigate the root causes, bug symptoms, test oracles, and the capabilities and limitations of existing testing techniques. This study took us substantial effort. It reveals several new interesting findings and implications which help shed light on future research on tackling functional bugs. Furthermore, findings from our study guided the design of a proof-of-concept differential testing tool, RegDroid, to automatically find functional bugs in Android apps. We applied RegDroid on 5 real-world popular apps, and successfully discovered 14 functional bugs, 10 of which were previously unknown and affected the latest released versions—all these 10 bugs have been confirmed and fixed by the app developers. Specifically, 10 out of these 14 found bugs cannot be found by existing testing techniques. We have made all the artifacts (including the dataset of 399 functional bugs and RegDroid) in our work publicly available at https://github.com/Android-Functional-bugs-study/home.

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Obviously, functional correctness is critical for an app’s success. However, in addition to crash bugs, non-crashing functional bugs (in short as “non-crashing bugs” in this work) like inadvertent function failures, silent user data lost and incorrect display information are prevalent, even in popular, well-tested apps. These non-crashing functional bugs are usually caused by program logic errors and manifest themselves on the graphic user interfaces (GUIs). In practice, such bugs pose significant challenges in effectively detecting them because (1) current practices heavily rely on expensive, small-scale manual validation ( the lack of automation ); and (2) modern fully automated testing has been limited to crash bugs ( the lack of test oracles ). This paper fills this gap by introducing independent view fuzzing , a novel, fully automated approach for detecting non-crashing functional bugs in Android apps. Inspired by metamorphic testing, 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 for further manual confirmation. We have realized our approach as an automated, end-to-end functional fuzzing tool, Genie. Given an app, (1) Genie automatically detects non-crashing bugs without requiring human-provided tests and oracles (thus fully automated ); and (2) the detected non-crashing bugs are diverse (thus general and not limited to specific functional properties ), which set Genie apart from prior work. We have evaluated Genie on 12 real-world Android apps and successfully uncovered 34 previously unknown non-crashing bugs in their latest releases — all have been confirmed, and 22 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 effectiveness. According to our analysis, Genie achieves a reasonable true positive rate of 40.9%, while these 34 non-crashing bugs could not be detected by prior fully automated GUI testing tools (as our evaluation confirms). Thus, our work complements and enhances existing manual testing and fully automated testing for crash bugs.