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A Sound Flow-Sensitive Heap Abstraction for the Static Analysis of Android Applications

机译:Android应用程序静态分析的声音敏感堆抽象

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The present paper proposes the first static analysis for Android applications which is both flow-sensitive on the heap abstraction and provably sound with respect to a rich formal model of the Android platform. We formulate the analysis as a set of Horn clauses defining a sound over-approximation of the semantics of the Android application to analyse, borrowing ideas from recency abstraction and extending them to our concurrent setting. Moreover, we implement the analysis in HornDroid, a state-of-the-art information flow analyser for Android applications. Our extension allows HornDroid to perform strong updates on heap-allocated data structures, thus significantly increasing its precision, without sacrificing its soundness guarantees. We test our implementation on DroidBeneh, a popular benchmark of Android applications developed by the research community, and we show that our changes to HornDroid lead to an improvement in the precision of the tool, while having only a moderate cost in terms of efficiency. Finally, we assess the scalability of our tool to the analysis of real applications.
机译:本文提出了Android应用程序的第一个静态分析,这在堆抽象上的流动敏感,以及关于Android平台的丰富的正式模型。我们将分析分析为一组喇叭子句,定义了Android应用程序的语义的声音过度逼近,以分析,借用从新复抽象和将它们扩展到我们的并发设置。此外,我们在Horndroid中实施分析,是Android应用的最先进的信息流分析仪。我们的扩展允许Horndroid对堆分配的数据结构进行强大的更新,从而显着提高其精度,而不会牺牲其声音担保。我们在研究界开发的Android应用程序的流行基准上测试了我们的实施,我们表明我们对Horndroid的变化导致工具精度的改善,同时只有效率的适度成本。最后,我们评估了我们工具的可扩展性,以分析了实际应用。

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