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Social coding platforms, such as GitHub, can serve as natural laboratoriesfor studying the diffusion of innovation through tracking the pattern of codeadoption by programmers. This paper focuses on the problem of predicting thepopularity of software repositories over time; our aim is to forecast the timeseries of popularity-related events (code forks and watches). In particular, weare interested in cross-repository patterns-how do events on one repositoryaffect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrentneural network integrates events across multiple active repositories,outperforming a standard ARIMA (Auto-Regressive Integrated Moving Average) timeseries prediction based on the single repository. The ability of the LSTM toleverage cross-repository information gives it a significant edge over standardtime series forecasting.

A Cross-Repository Model for Predicting Popularity in GitHub