Runtime Permission Issues in Android Apps: Taxonomy, Practices, and Ways Forward

Abstract

Android introduces a new permission model that allows apps to request permissions at runtime rather than at the installation time since 6.0 (Marshmallow, API level 23). While this runtime permission model provides users with greater flexibility in controlling an app's access to sensitive data and system features, it brings new challenges to app development. First, as users may grant or revoke permissions at any time while they are using an app, developers need to ensure that the app properly checks and requests required permissions before invoking any permission-protected APIs. Second, Android's permission mechanism keeps evolving and getting customized by device manufacturers. Developers are expected to comprehensively test their apps on different Android versions and device models to make sure permissions are properly requested in all situations. Unfortunately, these requirements are often impractical for developers. In practice, many Android apps suffer from various runtime permission issues (ARP issues). While existing studies have explored ARP issues, the understanding of such issues is still preliminary. To better characterize ARP issues, we performed an empirical study using 135 Stack Overflow posts that discuss ARP issues and 199 real ARP issues archived in popular open-source Android projects on GitHub. Via analyzing the data, we observed 11 types of ARP issues that commonly occur in Android apps. For each type of issues, we systematically studied: (1) how they can be manifested, (2) how pervasive and serious they are in real-world apps, and (3) how they can be fixed. We also analyzed the evolution trend of different types of issues from 2015 to 2020 to understand their impact on the Android ecosystem. Furthermore, we conducted a field survey and in-depth interviews among the practitioners from open-source community and industry, to gain insights from practitioners’ practices and learn their requirements of tools that can help combat ARP issues. Finally, to understand the strengths and weaknesses of the existing tools that can detect ARP issues, we built <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARPBench</small> , an open benchmark consisting of 94 real ARP issues, and evaluated the performance of three available tools. The experimental results indicate that the existing tools have very limited supports for detecting our observed issue types and report a large number of false alarms. We further analyzed the tools’ limitations and summarized the challenges of designing an effective ARP issue detection technique. We hope that our findings can shed light on future research and provide useful guidance to practitioners.