Learning user interface element interactions
Learning user interface element interactions
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DOI：
10.1145/3293882.3330569
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Christian Degott;N. P. Borges;A. Zeller
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文献类型：
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作者：
Christian Degott;N. P. Borges;A. Zeller
当生成图形用户界面的测试时，一个中心问题是确定如何与单个UI元素进行交互 - 点击，长或右键，刷卡，拖动，拖动，打字或更多。自动学习哪些互动可以用于哪些元素，并使用此信息来指导测试生成。在我们的评估中，测试生成有没有依赖于先前的知识，可以在我们的评估中获得更高的覆盖范围。 。
When generating tests for graphical user interfaces, one central problem is to identify how individual UI elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).