User stories play an important role in agile development systems. In this study, a method of summarising user stories is proposed to reuse them in the future. To enhance the results, quality improvement should be made on user stories. It would help developers build better results, and it may also lead to omitting some essential information. To avoid such issues, user stories are duplicated in two exact similar groups, and quality improvement is made on one set while the other set remains unattained. With the help of a modified bag of words and a verb parser, a collection of keywords and key verbs are extracted for both groups. Afterwards, automatic user stories are made, and then an expert improves them. Next, some experts choose between the results and select the better ones. The result is evaluated by applying different experiments on the framework and prototype implementation on 14 data sets of a user story from industry and a fake data set from Duke University. The result showed 97% of micro F -measure and 93% of macro F -measure, which are promising. These new user stories can be used as the base user stories in future similar projects.

中文翻译：

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自动汇总用户故事，以便在以后的类似项目中重复使用

用户案例在敏捷开发系统中起着重要作用。在这项研究中，提出了一种汇总用户故事的方法，以在将来重用它们。为了提高结果，应该对用户故事进行质量改进。这将有助于开发人员获得更好的结果，也可能导致省略一些基本信息。为避免此类问题，将用户故事复制到两个完全相似的组中，并且在一组上进行质量改进，而在另一组上保持质量。借助修改过的单词袋和动词解析器，可以为两组提取关键字和关键动词的集合。之后，自动制作用户故事，然后由专家对其进行改进。接下来，一些专家在结果之间进行选择，然后选择更好的结果。通过对来自行业的用户故事的14个数据集和来自杜克大学的假数据集进行不同的框架和原型实现实验，评估结果。结果显示97％的微F 量和宏的93％ F -措施，这是有希望的。这些新的用户故事可以在以后的类似项目中用作基本用户故​​事。