从非结构化文档中提取软件需求
Extracting Software Requirements from Unstructured Documents
摘要：
文本文档或提取中的需求识别是一项繁琐且容易出错的任务，许多研究人员建议将其自动化。我们手动注释了PURE数据集，从而创建了一个包含需求和非需求的新数据集。使用这个数据集，我们微调了BERT模型，并将结果与fastText和ELMo等几个基线进行比较。为了评估模型的语义更复杂的文件，我们比较纯数据集的结果与实验的请求信息（RFI）文件。RFI通常包括软件要求，但以不太标准化的方式。微调后的BERT在PURE数据集上的二进制句子分类任务上显示出有希望的结果。与以前和最近的研究相比，处理约束输入，我们的方法表现出高性能的精度和召回指标，而不可知的非结构化文本输入。
英文摘要：
Requirements identification in textual documents or extraction is a tedious and error prone task that many researchers suggest automating. We manually annotated the PURE dataset and thus created a new one containing both requirements and non-requirements. Using this dataset, we fine-tuned the BERT model and compare the results with several baselines such as fastText and ELMo. In order to evaluate the model on semantically more complex documents we compare the PURE dataset results with experiments on Request For Information (RFI) documents. The RFIs often include software requirements, but in a less standardized way. The fine-tuned BERT showed promising results on PURE dataset on the binary sentence classification task. Comparing with previous and recent studies dealing with constrained inputs, our approach demonstrates high performance in terms of precision and recall metrics, while being agnostic to the unstructured textual input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
