Stakeholders quantification plays a basic role in selecting the appropriate requirements because their judgement is a major criteria since not all of them have the same importance. Original proposals quantified stakeholders assigning them a weight. Nonetheless, actual projects manage a numerous stakeholders community hindering the inclusion of all their weights. This work proposes grouping strategies as means to reduce the number of stakeholders to manage in requirements selection keeping a proper coverage (i.e. howthe selection fulfils stakeholder demands). Our approach is based on stakeholders' salience, defined in terms of power, legitimacy and urgency attributes. Diverse strategies are applied selecting important stakeholders groups in a specific project. We use k-means}, k-medoids and hierarchical clustering, after deciding the number of cluster (4 and 3) based on validation indices. Either for all the stakeholders and each important group several requirements selection optimization problems have been solved. Tests find no significant differences for coverage when important stakeholders are filtered using clustering, regardless of the technique and number of groups, with a reduction between 66.32% to 87.75% in the number of stakeholders being considered. Applying clustering methods on data obtained from a real-world project is useful to identify the group of important stakeholders. The number of groups suggested matches the stakeholders theory and the coverage values in the requirements selection is kept.
翻译:摘要:干系人的量化在选定适当需求时发挥着基础性的作用,因为他们的判断是一个主要的标准,但并不是所有干系人都具有相同的重要性。早期的建议把干系人量化分配给它们一个权重。然而,现在的项目管理众多的干系人群体,使所有利益相关者的权重分配变得困难。本文提出了分组策略,以减少需求选择中需要管理的利益相关者数量,同时保持适当的涵盖范围(即选择如何满足利益相关者的要求)。我们的方法基于利益相关者的显著性,以权力,合法性和紧迫性属性定义。多种策略用于选择该特定项目中的重要利益相关者群体。我们使用k-means、k-medoids和层次集群,在确定集群数(4和3)时基于验证指数。针对所有利益相关者和每个重要群组,已解决了多个优化问题的需求选择。测试发现,使用聚类对重要利益相关方进行筛选时,无论采用哪种技术和多少个群组,覆盖范围没有显着差异,在考虑的利益相关者数量中减少了66.32%至87.75%。在来自现实项目的数据上应用聚类方法有助于识别重要利益相关方群体。建议的群组数符合干系人理论,并且需求选择中的涵盖值得到了保持。