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Using Tools to Assist Identification of Non-requirements in Requirements Specifications - A Controlled Experiment

机译:使用工具来帮助识别要求规格的非要求 - 受控实验

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[Context and motivation] In many companies, textual fragments in specification documents are categorized into requirements and non-requirements. This categorization is important for determining liability, deriving test cases, and many more decisions. In practice, this categorization is usually performed manually, which makes it labor-intensive and error-prone. [Question/problem] We have developed a tool to assist users in this task by providing warnings based on classification using neural networks. However, we currently do not know whether using the tool actually helps increasing the classification quality compared to not using the tool. [Principal idea/results] Therefore, we performed a controlled experiment with two groups of students. One group used the tool for a given task, whereas the other did not. By comparing the performance of both groups, we can assess in which scenarios the application of our tool is beneficial. [Contribution] The results show that the application of an automated classification approach may provide benefits, given that the accuracy is high enough.
机译:[语境和动机]在许多公司中,规范文件中的文本碎片分为要求和非要求。该分类对于确定责任,导出测试用例以及更多的决策非常重要。在实践中,该分类通常是手动执行的,这使得其劳动密集型和容易出错。 [问题/问题]我们开发了一种工具,可以通过使用神经网络的分类提供警告来帮助用户提供警告。但是,我们目前不知道使用该工具是否实际上有助于增加分类质量,而不是不使用该工具。 [主要思想/结果]因此,我们进行了两组学生的受控实验。一组用于给定任务的工具,而另一组没有。通过比较这两个组的性能,我们可以评估哪些场景我们工具的应用是有益的。 [贡献]结果表明,鉴于准确性足够高,可以提供自动分类方法的应用可以提供益处。

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