GitHub Pull Request Quality Dataset

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Jonathan Shaffer
DOI: 10.5281/zenodo.18262499
Cited by: 0

Abstract

A dataset of 79,058 merged pull requests from 24 popular open-source GitHub repositories with 142 engineered features for predicting PR review time. Features include code complexity metrics, NLP sentiment analysis (VADER, TextBlob), readability scores (Flesch-Kincaid, Gunning Fog), author experience profiles, and PR quality indicators. Target variables: review_time_hours (regression) review_speed_category (classification: fast/medium/slow) Baseline XGBoost performance: R²=0.38, Accuracy=69% Data collected via GitHub REST API, November-December 2025.

Fields of Study & Subjects

Software Engineering Research Open Source Software Innovations Software Engineering Techniques and Practices

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 January 2026
Language
EN
Document type
Dataset