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Predicting Project Health for Open Source Project

Software developed on public platforms are a source of data that can be used to make predictions about those projects. While the activity of a single developer may be random and hard to predict, when large groups of developers work together on software projects, the resulting behavior can be predicted with good accuracy.

To demonstrate this, we use 78,455 months of data from 1,628 GitHub projects to make various predictions about the current 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 (usually more than 50% wrong, sometimes over 130% wrong, median values). But that error rate can be greatly reduced using the DECART hyperparameter optimization. DECART is a differential evolution (DE) algorithm that tunes the CART data mining system to the particular details of a specific project.

To the best of our knowledge, this is the largest study yet conducted, using the most recent data, for predicting multiple health indicators of open-source projects. Further, due to our use of hyperparameter optimization, it may be the most successful. Our predictions have less than 10% error (median value) which is much smaller than the errors seen in related work.

Our results are a compelling argument for open-sourced development. Companies that only build in-house proprietary products may be cutting themselves off from the information needed to reason about those projects.

Links

• Paper
• Data
• RQ1: doc; src
• RQ2: doc; src
• ...

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