论文标题

学到的经验教训是为了改善涉及数据科学和过程自动化的敏捷项目的UX实践

Lessons Learned to Improve the UX Practices in Agile Projects Involving Data Science and Process Automation

论文作者

Ferreira, Bruna, Marques, Silvio, Kalinowski, Marcos, Lopes, Helio, Barbosa, Simone D. J.

论文摘要

上下文:以用户为中心的设计和敏捷方法论专注于人类问题。然而,敏捷的方法论专注于与签约客户的联系并为他们创造价值。通常,最终用户和敏捷团队之间的通信是由客户介导的。但是,他们不知道最终用户在日常工作中面临的问题。因此,通常仅在实施后,在用户测试和验证期间才能确定UX问题。目的:旨在提高敏捷项目中问题的理解和定义,该研究调查了敏捷团队在数据科学和过程自动化项目开发过程中所经历的实践和困难。此外,我们分析了团队对用户参与这些项目的看法。方法:我们从学术界 - 行业合作中收集了四个敏捷团队的数据,重点是提供数据科学和过程自动化解决方案。因此,我们应用了一份精心设计的问卷,该问卷由开发人员,Scrum Masters和UX设计师回答。总共有18名受试者回答了问卷。结果:从结果来看,我们确定了团队用来定义和理解问题并表示解决方案的实践。最常使用的实践是与利益相关者的原型和会议。帮助团队理解问题的另一种做法是使用精益的构成。同样,我们的结果提出了有关数据科学项目的一些具体问题。结论:我们观察到,最终用户参与对于理解和定义问题至关重要。它们有助于定义实施中域和障碍的要素。我们确定了有助于促进数据科学项目中用户团队通信的方法,并需要更详细的需求表示以支持数据科学解决方案。

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the agile team is mediated by customers. However, they do not know the problems end users face in their routines. Hence, UX issues are typically identified only after the implementation, during user testing and validation. Objective: Aiming to improve the understanding and definition of the problem in agile projects, this research investigates the practices and difficulties experienced by agile teams during the development of data science and process automation projects. Also, we analyze the benefits and the teams' perceptions regarding user participation in these projects. Method: We collected data from four agile teams in an academia-industry collaboration focusing on delivering data science and process automation solutions. Therefore, we applied a carefully designed questionnaire answered by developers, scrum masters, and UX designers. In total, 18 subjects answered the questionnaire. Results: From the results, we identify practices used by the teams to define and understand the problem and to represent the solution. The practices most often used are prototypes and meetings with stakeholders. Another practice that helped the team to understand the problem was using Lean Inceptions. Also, our results present some specific issues regarding data science projects. Conclusion: We observed that end-user participation can be critical to understanding and defining the problem. They help to define elements of the domain and barriers in the implementation. We identified a need for approaches that facilitate user-team communication in data science projects and the need for more detailed requirements representations to support data science solutions.

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