Launch-Mode-Aware Context-Sensitive Activity Transition Analysis

Yifei Zhang, Yulei Sui, Jingling Xue
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引用次数: 21

Abstract

Existing static analyses model activity transitions in Android apps context-insensitively, making it impossible to distinguish different activity launch modes, reducing the pointer analysis precision for an activity's callbacks, and potentially resulting in infeasible activity transition paths. In this paper, we introduce Chime, a launch-mode-aware context-sensitive activity transition analysis that models different instances of an activity class according to its launch mode and the transitions between activities context-sensitively, by working together with an object-sensitive pointer analysis. Our evaluation shows that our context-sensitive activity transition analysis is more precise than its context-insensitive counterpart in capturing activity transitions, facilitating GUI testing, and improving the pointer analysis precision.
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启动模式感知上下文敏感活动转换分析
现有的静态分析在Android应用程序中对活动转换进行上下文不敏感的建模,使得无法区分不同的活动启动模式,降低了活动回调的指针分析精度,并可能导致不可行的活动转换路径。在本文中,我们介绍了Chime,一个启动模式感知上下文敏感的活动转换分析,它根据一个活动类的启动模式和活动之间的转换上下文敏感地建模不同的实例,通过与对象敏感的指针分析一起工作。我们的评估表明,在捕获活动转换、促进GUI测试和提高指针分析精度方面,上下文敏感的活动转换分析比上下文不敏感的活动转换分析更精确。
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