User story extraction from natural language for requirements elicitation: Identify software-related information from online news

IF 4.3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Information and Software Technology Pub Date : 2023-06-01 DOI:10.1016/j.infsof.2023.107195
Daniel Siahaan , Indra Kharisma Raharjana , Chastine Fatichah
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Abstract

Context

The user story is a popular artifact in agile software development. Extracting user stories is helpful for process improvement in requirements elicitation, closing limitations such as limited access, and uncovering new and unique domains. Most sources of requirements elicitation are available in natural language form. However, the approach to extracting user stories from natural language is still limited.

Objective

This study aims to extract user stories from natural language. It includes identifying the aspect of who (stakeholder), aspect of what (stakeholder's wants), and aspect of why (the reason why the aspect of what exists).

Method

This study used online news as a case study because information related to stakeholders and their needs is available. Aspects of who, what, and why are obtained using a rule-based approach using part-of-speech (POS) chunking, named entity recognition (NER), dependency parsing, WordNet, and BloomSoft.

Result

We found that online news tends to generate requirements with hard-goals or soft-goals types. In identifying aspects of who, we succeeded in increasing the F-score value by combining stakeholder identification methods according to the characteristics of online news. We also found that PUblic REquirements (PURE), domain specificity, and WordNet lexical names can significantly improve the extraction of software-related information in identifying the aspects of what.

Conclusion

This study demonstrates that information related to software requirements could arise from non-software-related artifacts such as online news.

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从自然语言中提取用户故事,用于需求引出:从在线新闻中识别与软件相关的信息
上下文用户故事是敏捷软件开发中一个流行的工件。提取用户故事有助于改进需求获取过程,消除访问受限等限制,并发现新的和独特的领域。大多数需求启发的来源都是以自然语言的形式提供的。然而,从自然语言中提取用户故事的方法仍然有限。目的本研究旨在从自然语言中提取用户故事。它包括确定谁(利益相关者)的方面、什么(利益相关方想要的)的方面和为什么(存在这个方面的原因)的方面。方法本研究采用网络新闻作为案例研究,因为可以获得与利益相关者及其需求相关的信息。使用基于规则的方法,使用词性(POS)分块、命名实体识别(NER)、依赖解析、WordNet和BloomSoft,可以获得关于谁、什么和为什么的方面。结果我们发现,在线新闻倾向于生成具有硬目标或软目标类型的需求。在识别谁的方面,我们根据在线新闻的特点,结合利益相关者识别方法,成功地提高了F分值。我们还发现,PUblic RErequirements(PURE)、域特异性和WordNet词汇名称可以显著提高软件相关信息的提取,从而识别内容的各个方面。结论本研究表明,与软件需求相关的信息可能来自非软件相关的工件,如在线新闻。
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来源期刊
Information and Software Technology
Information and Software Technology 工程技术-计算机:软件工程
CiteScore
9.10
自引率
7.70%
发文量
164
审稿时长
9.6 weeks
期刊介绍: Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.
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