Team Discussions and Dynamics During DevOps Tool Adoptions in OSS Projects

In Open Source Software (OSS) projects, pre-built tools dominate DevOps-oriented pipelines. In practice, a multitude of configuration management, cloud-based continuous integration, and automated deployment tools exist, and often more than one for each task. Tools are adopted (and given up) by OSS projects regularly. Prior work has shown that some tool adoptions are preceded by discussions, and that tool adoptions can result in benefits to the project. But important questions remain: how do teams decide to adopt a tool? What is discussed before the adoption and for how long? And, what team characteristics are determinant of the adoption?In this paper, we employ a large-scale empirical study in order to characterize the team discussions and to discern the team-level determinants of tool adoption into OSS projects' development pipelines. Guided by theories of team and individual motivations and dynamics, we perform exploratory data analyses, do deep-dive case studies, and develop regression models to learn the determinants of adoption and discussion length, and the direction of their effect on the adoption. From data of commit and comment traces of large-scale GitHub projects, our models find that prior exposure to a tool and member involvement are positively associated with the tool adoption, while longer discussions and the number of newer team members associate negatively. These results can provide guidance beyond the technical appropriateness for the timeliness of tool adoptions in diverse programmer teams. Our data and code is available at https://github.com/lkyin/tool_adoptions. In this paper, we employ a large-scale empirical study in order to characterize the team discussions and to discern the team-level determinants of tool adoption into OSS projects' development pipelines. Guided by theories of team and individual motivations and dynamics, we perform exploratory data analyses, do deep-dive case studies, and develop regression models to learn the determinants of adoption and discussion length, and the direction of their effect on the adoption. From data of commit and comment traces of large-scale GitHub projects, our models find that prior exposure to a tool and member involvement are positively associated with the tool adoption, while longer discussions and the number of newer team members associate negatively. These results can provide guidance beyond the technical appropriateness for the timeliness of tool adoptions in diverse programmer teams. Our data and code is available at https://github.com/lkyin/tool_adoptions.