引导的,随机的基于模型的Android应用GUI测试

Ting Su, Guozhu Meng, Yuting Chen, Ke Wu, W. Yang, Yao Yao, G. Pu, Yang Liu, Z. Su
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引用次数: 268

摘要

移动应用无处不在,在复杂的环境中运行,并且是在上市时间的压力下开发的。因此,确保它们的正确性和可靠性成为一个重要的挑战。本文介绍了一种新的引导方法Stoat,用于在Android应用程序上进行基于随机模型的测试。Stoat分为两个阶段:(1)给定一个应用程序作为输入,它使用动态分析,通过加权UI探索策略和静态分析来增强应用程序GUI交互的随机模型;(2)采用Gibbs抽样对随机模型进行迭代突变/改进,并指导从突变模型生成的测试,以实现高代码和模型覆盖率,并展示多样化的序列。在测试过程中,随机注入系统级事件,进一步提高测试效率。Stoat在93个开源应用程序中进行了评估。结果表明:(1)与现有建模工具相比,Stoat模型的代码覆盖量增加了17% ~31%;(2)与Monkey和Sapienz这两种最先进的测试工具相比,Stoat检测到的独特崩溃多出3倍。此外,Stoat测试了1661个最受欢迎的b谷歌Play应用程序,并检测到2110个以前未知的独特崩溃。到目前为止,已经有43个开发者回应说他们正在调查我们的报告。报告的崩溃中有20个已经确认,8个已经修复。
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Guided, stochastic model-based GUI testing of Android apps
Mobile apps are ubiquitous, operate in complex environments and are developed under the time-to-market pressure. Ensuring their correctness and reliability thus becomes an important challenge. This paper introduces Stoat, a novel guided approach to perform stochastic model-based testing on Android apps. Stoat operates in two phases: (1) Given an app as input, it uses dynamic analysis enhanced by a weighted UI exploration strategy and static analysis to reverse engineer a stochastic model of the app's GUI interactions; and (2) it adapts Gibbs sampling to iteratively mutate/refine the stochastic model and guides test generation from the mutated models toward achieving high code and model coverage and exhibiting diverse sequences. During testing, system-level events are randomly injected to further enhance the testing effectiveness. Stoat was evaluated on 93 open-source apps. The results show (1) the models produced by Stoat cover 17~31% more code than those by existing modeling tools; (2) Stoat detects 3X more unique crashes than two state-of-the-art testing tools, Monkey and Sapienz. Furthermore, Stoat tested 1661 most popular Google Play apps, and detected 2110 previously unknown and unique crashes. So far, 43 developers have responded that they are investigating our reports. 20 of reported crashes have been confirmed, and 8 already fixed.
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