Investigating the Dynamics of Free/Libre Open Source Software Development Teams

Syracuse University, Syracuse NY

Investigators

Abstract

This is a multi-disciplinary and inter-disciplinary study (social and computer science) in the context of teams of Free/Libre Open Source (FLOSS) software developers to better understand the cognitive and social structures that underlie changes in individual and team behaviors in these teams. It addresses the general research question: What are the dynamics through which self-organizing distributed teams develop and work? Increasingly, organizational work is performed by distributed teams of interdependent knowledge workers. Such teams have many benefits, but geographic, organizational and social distance between members makes it difficult for team members to create the shared understandings and social structures necessary to be effective. But as yet, research and practitioner communities know little about the dynamics of distributed teams, especially not self-organizing ones. This research will study how distributed teams develop shared mental models to guide members' behavior, roles to mediate access to resources, and norms and rules to shape action, as well as the dynamics by which independent, geographically-dispersed individuals are socialized into these teams. As a basis for this study, a conceptual framework will be developed that uses a structurational perspective to integrate research on team behavior, communities of practice and shared mental models. A key innovation of this project is the integration of three methods to investigate these dynamics: natural language processing and social network analysis of transcripts of team interactions and source code analysis. The work will be carried out by a multi-disciplinary team including researchers from the fields of information systems and natural language processing, and with participation of an international collaborator at Politechnic of Bari. The research will be guided by an advisory board of FLOSS developers to ensure relevance and to promote diffusion of the findings into practice. This study will have conceptual, methodological as well as practical contributions. Developing an integrated theoretical framework to understand the dynamics of a distributed team will be a contribution to the study of distributed teams. The project will advance knowledge and understanding of FLOSS development and distributed work more generally by identifying how these teams evolve and how new members are socialized. Understanding the dynamics of structure and action in these teams is important to improve the effectiveness of FLOSS teams, software development teams, and distributed teams in general. The study fills a gap in the literature with an in-depth investigation of the practices adopted by FLOSS teams based on a large pool of data and a strong conceptual framework. Furthermore, the research will use several different techniques to analyze these practices, and thus provide a richer portrait of the dynamics of these development teams. The project will benefit society by suggesting ways to strengthen distributed FLOSS teams, an increasingly important approach to software development. The study will shed light on distributed work teams more generally, which will be valuable for managers who intend to implement this novel, technology-supported organizational form in practice. Findings from the study might also be used to enhance the way computer-mediated communication technologies (CMC) are used to support distance education or scientific collaboration, which are emerging applications of distributed teams. In order to improve infrastructure for research, the tools and data will be made available to other researchers. As well, the project involves an international collaboration. Such exchanges expand the perspectives, knowledge and skills of both groups of scientists. Finally, the project will promote teaching, training, and learning by providing an opportunity for students to work on research teams, utilize their competencies and develop new skills in data collection and analysis.