Wouters Jelle, Menkveld Abel, Brinkkemper Sjaak, Dalpiaz Fabiano
Royal Netherlands Marechaussee, The Hague, The Netherlands.
Tournify, Amsterdam, The Netherlands.
Requir Eng. 2022;27(4):429-455. doi: 10.1007/s00766-022-00384-6. Epub 2022 Aug 20.
Crowd-based Requirements Engineering (CrowdRE) promotes the active involvement of a large number of stakeholders in RE activities. A prominent strand of CrowdRE research concerns the creation and use of online platforms for a crowd of stakeholders to formulate ideas, which serve as an additional input for requirements elicitation. Most of the reported case studies are of small size, and they analyze the size of the crowd, rather than the quality of the collected ideas. By means of an iterative design that includes three case studies conducted at two organizations, we present the CREUS method for crowd-based elicitation via user stories. Besides reporting the details of these case studies and quantitative results on the number of participants, ideas, votes, etc., a key contribution of this paper is a qualitative analysis of the elicited ideas. To analyze the quality of the user stories, we apply criteria from the Quality User Story framework, we calculate automated text readability metrics, and we check for the presence of vague words. We also study whether the user stories can be linked to software qualities, and the specificity of the ideas. Based on the results, we distill six key findings regarding CREUS and, more generally, for CrowdRE via pull feedback.
基于群体的需求工程（CrowdRE）促进大量利益相关者积极参与需求工程活动。CrowdRE研究的一个突出方向是创建和使用在线平台，让一群利益相关者能够提出想法，这些想法可作为需求获取的额外输入。大多数已报道的案例研究规模较小，且它们分析的是群体规模，而非所收集想法的质量。通过一种包括在两个组织进行的三个案例研究的迭代设计，我们提出了通过用户故事进行基于群体的需求获取的CREUS方法。除了报告这些案例研究的细节以及关于参与者数量、想法数量、投票数等的定量结果外，本文的一个关键贡献是对所引出想法的定性分析。为了分析用户故事的质量，我们应用来自质量用户故事框架的标准，计算自动文本可读性指标，并检查是否存在模糊词汇。我们还研究用户故事是否可以与软件质量相关联，以及想法的具体程度。基于这些结果，我们提炼出关于CREUS以及更一般地关于通过拉式反馈进行的CrowdRE的六个关键发现。
