Datasets:
Tasks:
Tabular Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1M<n<10M
Tags:
source-code
software-engineering
defect-prediction
transfer-learning
static-analysis
c-language
License:
File size: 2,056 Bytes
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---
annotations_creators:
- expert-generated
language:
- en
license: cc-by-4.0
pretty_name: Cross-Project Defect Prediction (CPDP) Dataset — C & Java Projects
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- source-code
- software-engineering
- defect-prediction
- transfer-learning
- static-analysis
- c-language
- java
task_categories:
- tabular-classification
task_ids:
- multi-class-classification
---
# 🧩 Cross-Project Defect Prediction (CPDP) Dataset — C & Java Projects
This repository hosts a **custom dataset** for **Cross-Project Defect Prediction (CPDP)** research, curated from a diverse collection of real-world open-source projects written in **C (441 projects)** and **Java (98 projects)**.
The dataset aims to support research on **software defect prediction, transfer learning**, and **imbalanced data handling** across heterogeneous programming environments.
---
## 📘 Overview
| Language | #Projects | Description |
|-----------|------------|-------------|
| **C** | 441 | Includes diverse open-source repositories from various domains |
| **Java** | 98 | Covers projects from academic domains collected from GitHub and other public repositories |
Each project folder typically includes:
- Source code files (`.c`, `.h`, `.java`)
- Bug/defect labels (where available)
- Metadata (e.g., LOC, complexity, commits)
- Preprocessed CSV feature files for ML models
---
## 🎯 Purpose
The dataset is designed for:
- **Cross-Project Defect Prediction (CPDP)**
- **Transfer Learning** and **Domain Adaptation** studies
- **Feature engineering** on static code metrics
- **Benchmarking** new software defect prediction models
---
## 🧠 Suggested Research Directions
- Comparison of **within-project vs cross-project** prediction accuracy
- Study of **language heterogeneity** in CPDP (C ↔ Java transfer)
- Use of **oversampling** or **class balancing** methods (e.g., SMOTE, OTOMO)
- Integration with **Bayesian Networks**, **Deep Learning**, or **Tensor-based** models
|