ConDyGNN: A Context-Aware Dynamic Graph Neural Network for Predicting Developer Churn in Open Source Communities

The popularity of open source has witnessed significant growth in the past two decades. The sustainability and success of open source projects rely heavily on the active community of contributors, who have the freedom to join or depart voluntarily. The churn of core developers can have detrimental effects on the continuous development and progress of these communities. Therefore, previous studies have aimed to identify the factors that influence developer churn and reveal its impact on open source communities. However, there is currently a lack of research on predicting whether a developer will leave or retain within a given timeframe. This article aims to bridge this gap by introducing a novel model called context-aware dynamic graph neural network (ConDyGNN), which is designed specifically for predicting developer churn in open source communities. By leveraging historical data, this model takes into consideration various factors such as developers’ activities, collaborative networks, and community context information. To capture both temporal dependencies and spatial correlations among different developers, temporal convolutional network (TCN) and graph convolutional network (GCN) modules are employed in ConDyGNN. Furthermore, a context-aware module based on a multi-head attention mechanism is proposed to effectively integrate macrolevel community information and enhance the predictive performance. The superiority of the proposed model is empirically demonstrated through extensive comparative experiments and an ablation study.