Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub

TL;DR

This paper presents automated methods to identify public development projects in large open source datasets like GitHub, improving sample selection accuracy and reducing manual effort significantly.

Contribution

It introduces both simple and complex models for automatic detection of development projects, addressing scalability issues in dataset quality assurance.

Findings

Simple model achieves 0.827 precision and 0.947 recall.

Complex model reduces manual effort by 63.2% with high accuracy.

Models improve the efficiency and reliability of dataset sampling.

Abstract

Hosting over 10 million of software projects, GitHub is one of the most important data sources to study behavior of developers and software projects. However, with the increase of the size of open source datasets, the potential threats to mining these datasets have also grown. As the dataset grows, it becomes gradually unrealistic for human to confirm quality of all samples. Some studies have investigated this problem and provided solutions to avoid threats in sample selection, but some of these solutions (e.g., finding development projects) require human intervention. When the amount of data to be processed increases, these semi-automatic solutions become less useful since the effort in need for human intervention is far beyond affordable. To solve this problem, we investigated the GHTorrent dataset and proposed a method to detect public development projects. The results show that our method can effectively improve the sample selection process in two ways: (1) We provide a simple model to automatically select samples (with 0.827 precision and 0.947 recall); (2) We also offer a complex model to help researchers carefully screen samples (with 63.2% less effort than manually confirming all samples, and can achieve 0.926 precision and 0.959 recall).