Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades

• URL: http://arxiv.org/abs/2509.26173v2
• Date: Fri, 31 Oct 2025 12:27:12 GMT
• Title: Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
• Authors: Lisi Qarkaxhija, Maximilian Capraro, Stefan Menzel, Bernhard Sendhoff, Ingo Scholtes,
• Abstract summary: We analyze temporal activity patterns of developers, revealing an inherently bursty'' nature of commit contributions.<n>Our framework models social interactions, where a developer editing the code of other developers triggers accelerated activity among collaborators.<n>Our work sheds light on the emergent collective social dynamics in OSS communities and highlights the importance of activity cascades to understand developer churn and retention in collaborative software projects.
• Score: 3.061059269434306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the collective social behavior of software developers is crucial to model and predict the long-term dynamics and sustainability of Open Source Software (OSS) communities. To this end, we analyze temporal activity patterns of developers, revealing an inherently ``bursty'' nature of commit contributions. To investigate the social mechanisms behind this phenomenon, we adopt a network-based modelling framework that captures developer interactions through co-editing networks. Our framework models social interactions, where a developer editing the code of other developers triggers accelerated activity among collaborators. Using a large data set on 50 major OSS communities, we further develop a method that identifies activity cascades, i.e. the propagation of developer activity in the underlying co-editing network. Our results suggest that activity cascades are a statistically significant phenomenon in more than half of the studied projects. We further show that our insights can be used to develop a simple yet practical churn prediction method that forecasts which developers are likely to leave a project. Our work sheds light on the emergent collective social dynamics in OSS communities and highlights the importance of activity cascades to understand developer churn and retention in collaborative software projects.

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