GitEvolve: Predicting the Evolution of GitHub Repositories

TL;DR

GitEvolve is a deep learning system designed to predict user interactions, activity levels, and evolution trends of GitHub repositories, enhancing understanding of social dynamics on the platform.

Contribution

It introduces a multi-task deep neural network with graph-based representations to predict repository interactions and activity, including an artificial event type for activity modeling.

Findings

Effective prediction of user-group interactions

Accurate forecasting of repository activity levels

Model generalizes well to unseen users

Abstract

Software development is becoming increasingly open and collaborative with the advent of platforms such as GitHub. Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform. Previous work has mostly considered the dynamics of traditional social networking sites like Twitter and Facebook. We propose GitEvolve, a system to predict the evolution of GitHub repositories and the different ways by which users interact with them. To this end, we develop an end-to-end multi-task sequential deep neural network that given some seed events, simultaneously predicts which user-group is next going to interact with a given repository, what the type of the interaction is, and when it happens. To facilitate learning, we use graph based representation learning to encode relationship between repositories. We map users to groups by modelling common interests to better predict popularity and to generalize to unseen users during inference. We introduce an artificial event type to better model varying levels of activity of repositories in the dataset. The proposed multi-task architecture is generic and can be extended to model information diffusion in other social networks. In a series of experiments, we demonstrate the effectiveness of the proposed model, using multiple metrics and baselines. Qualitative analysis of the model's ability to predict popularity and forecast trends proves its applicability.