• DOI:10.1007/s10664-022-10171-0
• Corpus ID: 247594374

Predicting health indicators for open source projects (using hyperparameter optimization)

@article{Xia2020PredictingHI,
  title={Predicting health indicators for open source projects (using hyperparameter optimization)},
  author={Tianpei Xia and Wei Fu and Rui Shu and Rishabh Agrawal and Tim Menzies},
  journal={Empirical Software Engineering},
  year={2020},
  volume={27}
}

• Tianpei Xia, Wei Fu, +2 authors T. Menzies
• Published 12 June 2020
• Computer Science
• Empirical Software Engineering

Software developed on public platform is a source of data that can be used to make predictions about those projects. While the individual developing activity may be random and hard to predict, the developing behavior on project level can be predicted with good accuracy when large groups of developers work together on software projects. To demonstrate this, we use 64,181 months of data from 1,159 GitHub projects to make various predictions about the recent status of those projects (as of April 2020). We find that traditional estimation algorithms make many mistakes. Algorithms like k-nearest neighbors (KNN), support vector regression (SVR), random forest (RFT), linear regression (LNR), and regression trees (CART) have high error rates. But that error rate can be greatly reduced using hyperparameter optimization. To the best of our knowledge, this is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects. To facilitate open science (and replications and extensions of this work), all our materials are available online at https://github.com/arennax/Health_Indicator_Prediction.