GHPR Dataset
The GHPR dataset is used for empirical research and evaluation of software defect prediction. It is built from GitHub Pull Requests (PRs) and identifies 3,026 defect‑fix records. Each fix is treated as a record, yielding 6,052 learning instances (3,026 defective and 3,026 non‑defective). The dataset is provided in CSV and SQL formats and includes 16 features such as project name, project owner, project description, tags, programming language, pre‑ and post‑fix version IDs, defective code, commit description, commit time, pre‑ and post‑fix file contents, file‑path changes, PR title and description, etc.
Dataset description and usage context
GHPR Dataset Overview
Dataset Description
- Name: GHPR Dataset
- Purpose: Empirical research and evaluation of software defect prediction
- Record Count: 3,026 defect‑fix records based on GitHub Pull Requests (PRs)
- Learning Instances: 6,052 total (3,026 defective, 3,026 non‑defective)
Data Format
- File Formats: Two formats are provided
ghprdata.csv: Compatible with Python's NumPy or pandasghprdata.sql: UTF‑8 encoded, suitable for large‑scale databases
Data Features
- Record Features: Each record contains 16 features, including project name, project owner, project description, project tags, programming language, pre‑ and post‑fix version IDs, defective code, commit description, commit time, pre‑ and post‑fix file contents, file‑path changes, PR title and description, etc.
Dataset Metrics
- Static Metrics: 21 static metrics are calculated for the 6,052 instances, such as coupling, method complexity, inheritance depth, response classes, method cohesion, etc., computed using the open‑source tool mauricioaniche/ck.
Citation Information
- Citation Requirement: When using this dataset in publications, please cite the following paper:
- Authors: Jiaxi Xu, Fei Wang, Jun Ai
- Journal: IEEE Transactions on Reliability
- Title: Defect Prediction With Semantics and Context Features of Codes Based on Graph Representation Learning
- Year: 2021
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