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FragDroid: Automated User Interface Interaction with Activity and Fragment Analysis in Android Applications

机译:FragDroid:自动化的用户界面与Android应用程序中的活动和片段分析的交互

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Recent years have witnessed the enormous growth of Android phones in the consumer market. On the other hand, as the most popular mobile platform, Android also attracts lots of attackers' attention. As a result, more and more Android malicious apps appear in the wild, which poses a serious threat to user's security and privacy. To such massive volume of Android malware, automated UI testing techniques have become the mainstream solutions because of the detection efficiency and accuracy. However, all existing UI testing techniques treat the Activity as the basic unit of UI interactions and cannot carry out a fine-grained analysis for Fragments. Due to the lack of Fragment-level analysis, the path coverage is usually quite limited. To fill this gap, in this paper, we propose FragDroid, a novel automated UI testing framework supporting both Activity and Fragment analysis. To achieve the Fragment-level testing, we design the Activity & Fragment Transition Model (AFTM) to simulate the internal interactions of an app, and ATFM could be utilized to generate test cases automatically through UI interactions. With the assist of AFTM, FragDroid achieves accessing most Activities and Fragments contained in the app along with the capability of detecting arbitrary API calls. We implemented a prototype of FragDroid and evaluated it on 15 popular apps. The results show FragDroid successfully covered 66% Fragments and the corresponding API calls of testing apps. Also, the traditional approaches have to miss at least 9.6% of API calls invoked in Fragments.
机译:近年来见证了Android手机在消费市场中的巨大增长。另一方面,作为最受欢迎的移动平台,Android也吸引了很多攻击者的注意力。因此,越来越多的Android恶意应用程序出现在野外,这会对用户的安全和隐私构成严重威胁。对于这种大量的Android恶意软件,自动化UI测试技术已成为主流解决方案,因为检测效率和准确性。然而,所有现有的UI测试技术将活动视为UI相互作用的基本单元,并且不能对碎片进行细粒度分析。由于缺乏片段水平分析,路径覆盖率通常非常有限。为了填补这一差距,在本文中,我们提出了支持既有活动和片段分析的新型自动化UI测试框架的碎屑。为了实现片段级测试,我们设计活动和片段转换模型(AFTM)以模拟应用程序的内部交互,并且可以使用ATFM自动通过UI交互生成测试用例。通过AFTM的辅助,Fragdroid实现了应用程序中包含的大多数活动和片段以及检测任意API呼叫的能力。我们实施了碎屑的原型,并在15个受欢迎的应用程序中进行了评估。结果显示Fragdroid覆盖了66 \%片段和测试应用的相应API呼叫。此外,传统方法必须错过至少9.6 \%在碎片中调用的API调用。

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