Testing Apps With Real-World Inputs
Testing Apps With Real-World Inputs
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DOI：
10.1145/3387903.3389310
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Tanapuch Wanwarang;N. P. Borges;Leon Bettscheider;A. Zeller
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文献类型：
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作者：
Tanapuch Wanwarang;N. P. Borges;Leon Bettscheider;A. Zeller
要测试移动的应用程序，需要真实且一致的测试输入。Web测试的链接方法表明，DBPedia等知识库可以成为语义一致输入的可靠来源。在本文中，我们调整和扩展链接的方法，以测试生成的移动的应用程序：（1）我们确定和匹配的描述性标签与输入字段，基于人类感知的完形原则;（2）然后，我们使用自然语言处理技术来提取与标签相关的概念;（3）我们使用这个概念来查询知识库的候选输入值;（4）将UI元素按照功能分为输入和动作，先填充输入元素，再与动作交互，我们的评估表明，利用知识库测试具有真实输入的移动的应用是有效的。平均而言，我们的方法比随机生成的文本输入多覆盖9%的语句。
To test mobile apps, one requires realistic and coherent test inputs. The Link approach for Web testing has shown that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs. In this paper, we adapt and extend the Link approach towards test generation for mobile applications:(1)We identify and match descriptive labels with input fields, based on the Gestalt principles of human perception;(2)We then use natural language processing techniques to extract the concept associated with the label;(3)We use this concept to query a knowledge base for candidate input values;(4)We cluster the UI elements according to their functionality into input and actions, filling the input elements first and then interacting with the actions.Our evaluation shows that leveraging knowledge bases for testing mobile apps with realistic inputs is effective. On average, our approach covered 9% more statements than randomly generated text inputs.