A Cross-Repository Model for Predicting Popularity in GitHub

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 patternshow 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.

Analyzing Maintenance Activities of Software Libraries

“…Different studies predicted specific features for different topics, such as health related features [22,37] or popularity measures [4,35]. Predictions for multivariate maintenance activity features are missing so far.…”

“…Predictions for multivariate maintenance activity features are missing so far. Statistical algorithms such as logistic regression, k-nearest neighbors, support vector regression, linear regression and regression trees [22,37], but also neural network based algorithms, such as LSTM RNNs [4,35] were applied. The prediction periods range from 1 to 30 days [4,22,35,37], 1 to 6 months [4,22,37] and up to 12 months and longer [4,37].…”

“…Statistical algorithms such as logistic regression, k-nearest neighbors, support vector regression, linear regression and regression trees [22,37], but also neural network based algorithms, such as LSTM RNNs [4,35] were applied. The prediction periods range from 1 to 30 days [4,22,35,37], 1 to 6 months [4,22,37] and up to 12 months and longer [4,37]. The main gap of RQ3 is the integration of transitive links into the prediction approach since current approaches are based on project-level features only.…”