利用DomText-WMDS从在线新闻中提取软件需求相关信息

Mutia Rahmi Dewi, Indra Kharisma Raharjana, Daniel Siahaan, Nurul Jannah
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引用次数: 0

摘要

目前,对从非软件构件中提取软件需求进行评估的研究并不多。这些相关领域的大多数研究都集中在软件工件上,例如项目描述或作为需求提取来源的用户评审。本研究旨在利用向量空间模型从在线新闻中识别出与软件需求相关的信息。这些与软件需求相关的信息可以帮助系统分析人员根据在线新闻中涉众所呈现的经验发现问题域。本研究提出了DomText-WMDS从在线新闻中提取需求相关信息。我们使用在线新闻和公共软件需求规范数据集,使用领域专用性技术开发特定于软件的词汇表。然后我们对特定词汇软件进行扩展,通过对在线新闻文档建立向量空间模型来获得更全面的结果。这个更新版本的特定于软件的词汇表可用于软件需求相关信息的基本过滤,这些信息是以前使用词性分块(POS)提取的。该研究提高了软件需求相关信息提取的性能,与领域特异性方法相比,准确率和召回率分别为61.09%和60.66%,而后者仅能获得43.34%和40.78%。
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Extracting Software Requirements-Related Information from Online News using DomText-WMDS
Currently, there are not many studies that assess software requirements extraction from non-software artifacts. Most of the research in these related areas are focuses on software artifacts such as project descriptions or user reviews as a source of requirements extraction. This research aims to identify relevant information to the software requirements from online news using the vector space model. This software requirements-related information can assist systems analysts in discovering the problem domain based on the lesson learned presented by stakeholders in online news. This research proposes DomText-WMDS to extract requirements-related information from online news. We used online news and public software requirements specification dataset to develop software-specific vocabulary using domain specificity technique. Then we expanded the specific vocabulary software to obtain more comprehensive results by building vector space model from online news documents. This updated version of software-specific vocabulary can be used for basic filtering of software requirements-related information that previously extracted using the part-of-speech (POS) chunking. This study improved the performance for extracting software requirements-related information, with precision and recall 61.09% and 60.66% compared to domain specificity approach that only manages to obtain 43.34% and 40.78%.
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审稿时长
6 weeks
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