Seven Reasons Why: An In-Depth Study of the Limitations of Random Test Input Generation for Android

Farnaz Behrang, A. Orso
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引用次数: 2

Abstract

Experience paper: Testing of mobile apps is time-consuming and requires a great deal of manual effort. For this reason, industry and academic researchers have proposed a number of test input generation techniques for automating app testing. Although useful, these techniques have weaknesses and limitations that often prevent them from achieving high coverage. We believe that one of the reasons for these limitations is that tool developers tend to focus mainly on improving the strategy the techniques employ to explore app behavior, whereas limited effort has been put into investigating other ways to improve the performance of these techniques. To address this problem, and get a better understanding of the limitations of input-generation techniques for mobile apps, we conducted an in-depth study of the limitations of MONKEy-arguably the most widely used tool for automated testing of Android apps. Specifically, in our study, we manually analyzed Monkey's performance on a benchmark of 64 apps to identify the common limitations that prevent the tool from achieving better coverage results. We then assessed the coverage improvement that Monkey could achieve if these limitations were eliminated. In our analysis of the results, we also discuss whether other existing test input generation tools suffer from these common limitations and provide insights on how they could address them.
7个原因:Android随机测试输入生成局限性的深入研究
经验报告:测试手机应用非常耗时,需要大量的手工操作。出于这个原因,行业和学术研究人员已经提出了许多自动化应用测试的测试输入生成技术。尽管这些技术很有用,但它们也有缺点和局限性,常常使它们无法获得高覆盖率。我们认为,这些限制的原因之一是工具开发人员倾向于主要关注改进技术用于探索应用程序行为的策略,而有限的努力已经投入到研究其他方法来提高这些技术的性能。为了解决这个问题,并更好地理解移动应用程序的输入生成技术的局限性,我们对monkey的局限性进行了深入研究,monkey可以说是Android应用程序自动化测试中使用最广泛的工具。具体来说,在我们的研究中,我们在64个应用程序的基准测试中手动分析了Monkey的性能,以确定阻碍该工具获得更好覆盖率结果的常见限制。然后,我们评估了如果消除这些限制,Monkey可以实现的覆盖率改进。在我们对结果的分析中,我们还讨论了其他现有的测试输入生成工具是否遭受这些常见的限制,并提供了如何解决这些限制的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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