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User Story Estimation Based on the Complexity Decomposition Using Bayesian Networks
Programming and Computer Software ( IF 0.7 ) Pub Date : 2020-12-22 , DOI: 10.1134/s0361768820080095
M. Durán , R. Juárez-Ramírez , S. Jiménez , C. Tona

Abstract

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers.



中文翻译:

贝叶斯网络基于复杂度分解的用户故事估计

摘要

当前,在Scrum中,有许多方法可以根据工作量或复杂性来估计用户故事。大多数现有技术都考虑了细晶粒水平的因素。这些技术并不总是准确的。尽管规划扑克是Scrum中估计用户故事的最常用的方法,但是它主要在经验丰富的团队中有效,因为估计主要取决于专家的观察,但是当经验不足的团队使用时很难。在本文中,我们提出了在粗粒度水平上进行复杂度分解的提议,以便考虑复杂度估计的重要因素。我们使用贝叶斯网络来表示这些因素及其关系。网络边缘由专业从业人员对因素的重要性进行加权。网络的节点代表因素。在用户估计阶段,Scrum团队成员介绍每个因素的值;通过这种方式,网络为用户故事的复杂性生成了一个值,该值转换为代表故事点的Planning Poker卡号。这项研究的目的是向没有经验或没有历史数据的开发团队提供一种通过侧重于开发人员的人为因素的模型来估计用户故事的复杂性的方法。

更新日期:2020-12-22
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