Trend Prediction of GitHub using Time Series Analysis
T Varuna, Anuraj Mohan · 2019
GitHub is a web-based collaborative platform for software developers with around 28 million users and 57 million public repositories which promote social coding. GitHub is an evolving network with the developments in the projects and repositories. The underlying working principle of GitHub is based on the events occurring in a time-based manner. The number of events of a repository is proportional to its popularity. In this proposed work, we perform trend prediction on GitHub. The trend prediction is categorised into three tasks, repository trend prediction, language trend prediction and domain trend prediction respectively. Using most relevant and recurring events, such as Create, Fork, Pullrequest, Push and issue, a multivariate time series is constructed. Trends are predicted with time series forecasting using Long Short Term Memory(LSTM) model. The trend prediction results compare the repositories, languages and domains and help in identifying the trending ones. GitHub insights are taken to facilitate social coding and in selecting the relevant languages and domains for the upcoming projects.