Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
September 30, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lisi Qarkaxhija, Maximilian Capraro, Stefan Menzel, Bernhard Sendhoff, Ingo Scholtes
arXiv ID
2509.26173
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted