Automated test input generation for Android: are we really there yet in an industrial case?

Xia Zeng, Dengfeng Li, Wujie Zheng, Fan Xia, Yuetang Deng, Wing Lam, Wei Yang, Tao Xie
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引用次数: 83

Abstract

Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial practices) outperformed all of the research tools that they studied. In this paper, we present two significant extensions of that study. First, we conduct the first industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical findings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey, along with empirical insights for future enhancements to both Monkey and our approach.
Android的自动化测试输入生成:我们真的已经在工业案例中实现了吗?
考虑到越来越多的研究工具可以自动生成测试Android应用程序(或简单的应用程序)的输入,研究人员最近提出了一个问题:“我们还到了吗?”(就工具的实用性而言)。通过对各种工具进行实证研究,研究人员发现Monkey(这类工具在工业实践中使用最广泛)的表现优于他们研究的所有研究工具。在本文中,我们提出了该研究的两个重要扩展。首先,我们对每月活跃用户超过7.62亿的流行即时通讯应用微信应用Monkey进行了首次工业案例研究,并报告了Monkey在工业环境下的局限性的实证结果。其次,我们开发了一种新的方法来解决Monkey的主要局限性,并在Monkey的基础上实现了实质性的代码覆盖率改进,同时为Monkey和我们的方法的未来增强提供了经验见解。
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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