Android Fragmentation Research Articles

Taming Android Fragmentation through Lightweight Crowdsourced Testing

Android fragmentation refers to the overwhelming diversity of Android devices and OS versions. These lead to the impossibility of testing an app on every supported device, leaving a number of compatibility bugs scattered in the community and thereby resulting in poor user experiences. To mitigate this, our fellow researchers have designed various works to automatically detect such compatibility issues. However, the current state-of-the-art tools can only be used to detect specific kinds of compatibility issues (i.e., compatibility issues caused by API signature evolution), i.e., many other essential types of compatibility issues are still unrevealed. For example, customized OS versions on real devices and semantic changes of OS could lead to serious compatibility issues, which are non-trivial to be detected statically. To this end, we propose a novel, lightweight, crowdsourced testing approach, to fill this research gap and enable the possibility of taming Android fragmentation through crowdsourced efforts. Specifically, crowdsourced testing is an emerging alternative to conventional mobile testing mechanisms that allow developers to test their products on real devices to pinpoint platform-specific issues. Experimental results on thousands of test cases on real-world Android devices show that is effective in automatically identifying and verifying API-induced compatibility issues. Also, after investigating the user experience through qualitative metrics, users' satisfaction provides strong evidence that is useful and welcome in practice.

APP constraint analysis approach to select mobile devices for compatibility crowdtesting

AbstractThe compatibility issues caused by Android fragmentation have become a vital task in the development of Android applications. To locate those issues, thousand of crowd testers run apps on different devices with different configurations to achieve the largest coverage, which might be costly and time‐consuming. Since existing approaches to selecting optimal devices are device‐side analysis without the information of the internal structures of apps, app‐side analysis that flags the essential devices for testers has remained elusive. To mitigate this gap of compatibility crowdtesting, this paper proposes an app constraint analysis approach named CompatDroid to generate the optimal device set to guide crowd testers. By evaluating 46 benchmark apps on 14 SDK versions, the optimal device sets are successfully generated, and CompatDroid only needs no more than 7 Android versions to achieve almost the same code coverage (i.e., 33.13%) testing on all 14 android versions (i.e., 34.65%) in 36 of 46 apps, which indicates that it can drastically reduce the consumption of test resources while losing little test coverage. On a larger dataset, CompatDroid successfully analyzes 98.3% of 645 apps, in which the median number of the optimal SDK versions set is 2.5 versions, and 68.92% of those apps contain the constraints of SDK version (i.e., SDK version) while 84.86% of them do not have the constraints of hardware information (i.e., model name and manufacture name).

Platform Fragmentation and Complementary Innovation

Complementor innovation is an essential form of value co-creation in open platform ecosystems. However, the increasing platform fragmentation, i.e., divided users in the ecosystem adopting different versions of the platform technologies, has significantly hindered complementor innovation. Reducing platform fragmentation is a complex coordination problem involving several parties in the ecosystem, thus it is unclear whether and how a platform’s efforts to fight fragmentation would affect complementor innovation. Focusing on recent efforts by Google to address Android fragmentation, we find that while the platform investment does not immediately reduce Android fragmentation, app developers respond to the platform’s action by significantly increasing their innovation efforts in updating apps shortly after platform investment. This forward-looking behavior is likely due to developers’ anticipation of lowered innovation cost in the future, as the impact of platform investment is greater for developers with more variable cost structure such as a larger and more diverse user base, less development experience and lack of economies of scale (i.e., with a smaller portfolio of apps). The effect is also greater for developers in more competitive markets where gaining user base can be costly due to market competition. Our research highlights the role of platform commitment to improve platform infrastructure in complementor innovation, and provides implications for platform investment and intervention.

API compatibility issues in Android: Causes and effectiveness of data-driven detection techniques

Android fragmentation is a well-known issue referring to the adoption of different versions in the multitude of devices supporting such an operating system. Each Android version features a set of APIs provided to developers. These APIs are subject to changes and may cause compatibility issues. To support app developers, approaches have been proposed to automatically identify API compatibility issues. CiD, the state-of-the-art approach, is a data-driven solution learning how to detect those issues by analyzing the change history of Android APIs (“API side” learning). In this paper (extension of our MSR 2019 paper), we present an alternative data-driven approach, named ACRyL. ACRyL learns from changes implemented in apps in response to API changes (“client side” learning). When comparing these two solutions on 668 apps, for a total of 11,863 snapshots, we found that there is no clear winner, since the two techniques are highly complementary, and none of them provides a comprehensive support in detecting API compatibility issues: ACRyL achieves a precision of 7.0% (28.0%, when considering only the severe warnings), while CiD achieves a precision of 18.4%. This calls for more research in this field, and led us to run a second empirical study in which we manually analyze 500 pull-requests likely related to the fixing of compatibility issues, documenting the root cause behind the fixed issue. The most common causes are related to changes in the Android APIs ( $\sim $ 87%), while about 13% of the issues are related to external causes, such as build and distribution, dependencies, and the app itself. The provided empirical knowledge can inform the building of better tools for the detection of API compatibility issues.

Taming transitive permission attack via bytecode rewriting on Android application

AbstractGoogle Android is popular for mobile devices in recent years. The openness and popularity of Android make it a primary target for malware. Even though Android's security mechanisms could defend most malware, its permission model is vulnerable to transitive permission attack, a type of privilege escalation attacks. Many approaches have been proposed to detect this attack by modifying the Android OS. However, the Android's fragmentation problem and requiring rooting Android device hinder those approaches large‐scale adoption. In this paper, we present an instrumentation framework, called SEAPP, for Android applications (or “apps”) to detect the transitive permission attack on unmodified Android. SEAPP automatically rewrites an app without requiring its source codes and produces a security‐harden app. At runtime, call‐chains are built among these apps and detection process is executed before a privileged API is invoked. Our experimental results show that SEAPP could work on a large number of benign apps from the official Android market and malicious apps, with a repackaged success rate of over 99.8%. We also show that our framework effectively tracks call‐chains among apps and detects known transitive permission attack with low overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Designing Knowledge Base Mobile Application Compatibility Test System for Android Fragmentation

Android is an open operating system developed by the Open Handset Alliance (OHA) that is Google-led. Due to the nature of an open operating system, however, Android has the fragmentation problem that mobile application shows different behavior patterns in heterogeneous devices. To resolve this issue, a developer needs much cost in terms of time and efforts to devise application tests in mobile environment. In this paper, we propose two methods that handle android fragmentation problem. Moreover, utilizing the methods, we produce mobile application compatibility test system. We also perform case studies in order to evaluate the methods’ effectiveness concerning fragmentation problem by developing application. The proposed system analyses the fragmentation that is found in code level as well as in API level. The experimental results demonstrate enhancement in mobile application test process regarding time and cost.