# A Cross-Repository Model for Predicting Popularity in GitHub

## TL;DR

This paper introduces a cross-repository LSTM model that predicts the popularity of GitHub repositories by leveraging events across multiple repositories, outperforming traditional single-repository models.

## Contribution

The paper presents a novel LSTM-based approach that incorporates cross-repository data for more accurate popularity prediction in social coding platforms.

## Key findings

- LSTM model outperforms ARIMA in popularity prediction
- Cross-repository information improves forecasting accuracy
- Model captures influence of one repository's events on others

## Abstract

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