Avgust: automating usage-based test generation from videos of app executions
Avgust: automating usage-based test generation from videos of app executions
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DOI：
10.1145/3540250.3549134
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发表时间：
2022-09
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通讯作者：
Yixue Zhao;Saghar Talebipour;Kesina Baral;Hyojae Park;Leon Yee;Safwat Ali Khan;Yuriy Brun;N. Medvidović;Kevin Moran
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作者：
Yixue Zhao;Saghar Talebipour;Kesina Baral;Hyojae Park;Leon Yee;Safwat Ali Khan;Yuriy Brun;N. Medvidović;Kevin Moran
为移动应用程序的编写和维护UI测试是一项耗时的任务，而数十年的研究已经为UI测试生成了自动伴侣的方法，这些方法通常集中在崩溃或最大化代码范围内。已经表明，开发人员更喜欢基于用法的测试，这些测试围绕应用程序功能的特定用途，以帮助支持回归测试等活动。 UI屏幕和用户输入的语义。然后，Avgust使用此用途来综合新目标应用程序的测试用例。而且，该Avgust的分类器的表现优于最新技术。
Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art.