Software projects are not developed in isolation. Open source software projects encourage a networked collaboration and interdependence across projects and developers. Recent research has shifted to studying software ecosystems, communities of projects that depend on each other and are developed together. However, identifying technical dependencies at the ecosystem level can be challenging. In this dissertation, we propose a new method, known as reference coupling, for detecting technical dependencies between projects. The method establishes dependencies through user-specified cross-references between projects. We use our method to identify ecosystems in GitHub hosted projects, and we identify several characteristics of the identified ecosystems. Our findings show that most ecosystems are centered around one project and are interconnected with other ecosystems. The predominant type of ecosystems are those that develop tools to support software development. We also found that the project owners’ social behavior aligns well with the technical dependencies within the ecosystem, but project contributors’ social behavior does not align with these dependencies. We conclude with a discussion on future research that is enabled by our reference coupling method. / Graduate / harrison.franc@gmail.com

Coordination in software development, especially in global software development, is important because a team cannot perform well unless its team members communicate and maintain awareness of each other's activities. In order to improve socio-technical coordination, which is coordination among team members who work on interdependent technical entities, it must be conceptualized and measured. One measurement of coordination is socio-technical congruence, which calculates the alignment between technical relationships and social relationships. The problem is that there are a large number of social and technical factors to consider when using socio-technical congruence to study coordination. Current limitations with socio-technical congruence include the inability to represent the size of gaps in coordination between people, the sparse understanding of the role of awareness in conjunction with other coordination mechanisms, and the lack of a technique with which to model people who are involved in certain communication patterns, but not assigned to technical tasks. To address these limitations, this dissertation describes a socio-technical congruence model to study socio-technical coordination. The model focuses on refining conceptualizations of technical and social relationships between people, on describing an improved gap technique for calculating socio-technical alignment, and on providing guidelines on how to study coordination in teams using the socio-technical congruence model. I first develop the model theoretically from related work. I then conduct two empirical investigations to address limitations of the model. The first study examines awareness in a small global team using observational studies. The second study examines important communicators and people who emerge in coordination} despite having no technical relationships by examining email archives from the same team. I conduct a third empirical investigation of a large global team to apply the model to study the relationship between socio-technical congruence and team performance using the project's repository. Finally, I revisit the model and improve it based on the empirical findings. The model refines conceptualizations of relationships, classifies emergent people who are suddenly involved with a task or a team during the project, and represents multi-variable relationships. It includes a template and an accompanying process for applying socio-technical congruence to study socio-technical coordination. This model enables researchers to study socio-technical coordination and analyze its effect on software engineering outcomes such as performance and quality. / Graduate