The functional interaction structure of a team captures the preferences with which members of different roles interact. This paper presents a data-driven approach to detect the functional interaction structure for software development teams from traces team members leave on development platforms during their daily work. Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model to compute interaction preferences between members of different roles. We apply our approach in a case study to extract the functional interaction structure of a product team at the German IT security company genua GmbH. We subsequently validate the accuracy of the detected interaction structure in interviews with five team members. Finally, we show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios. Specifically, we evaluate the level of knowledge diffusion in the team and identify areas where the team can further improve. Our approach is computationally efficient and can be applied in real time to manage a team's interaction structure.

翻译：团队的功能性交互结构捕捉了不同角色成员之间的交互偏好。本文提出了一种数据驱动方法，通过团队成员在日常工作中于开发平台留下的痕迹，检测软件开发团队的功能性交互结构。该方法考虑了团队成员活动水平的差异，并采用块约束配置模型计算不同角色成员之间的交互偏好。我们在一项案例研究中应用该方法，提取了德国IT安全公司genua GmbH某产品团队的功能性交互结构。随后，通过与五位团队成员进行访谈，验证了检测到的交互结构的准确性。最后，我们展示了该方法如何使团队能够将自身功能性交互结构与合成生成的基准场景进行对比。具体而言，我们评估了团队中的知识扩散水平，并识别出团队可进一步改进的领域。该方法计算高效，可实时应用于管理团队的交互结构。