---
# YAML Metadata Block
language: 
  - en
tags:
  - vulnerability-detection
  - cve
  - code-changes
  - software-security
license: mit
dataset_info:
  features:
    - name: CVE_ID
      dtype: string
    - name: CWE_ID
      dtype: string
    - name: Score
      dtype: float
    - name: Summary
      dtype: string
    - name: commit_id
      dtype: string
    - name: codeLink
      dtype: string
    - name: file_name
      dtype: string
    - name: func_after
      dtype: string
    - name: lines_after
      dtype: string
  dataset_size: 10GB
---

# MSR Data Cleaned - C/C++ Code Vulnerability Dataset

[![Dataset License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)



## 📌 Dataset Description
A curated collection of C/C++ code vulnerabilities paired with:
- CVE details (scores, classifications, exploit status)
- Code changes (commit messages, added/deleted lines)
- File-level and function-level diffs

## 🔍 Sample Data Structure
```python
+---------------+-----------------+----------------------+---------------------------+
| CVE ID        | Attack Origin   | Publish Date         | Summary                   |
+===============+=================+======================+===========================+
| CVE-2015-8467 | Remote          | 2015-12-29           | "The samldb_check_user..."|
+---------------+-----------------+----------------------+---------------------------+
| CVE-2016-1234 | Local           | 2016-01-15           | "Buffer overflow in..."   |
+---------------+-----------------+----------------------+---------------------------+
```

## 🛠️ Usage Instructions

### 1. Accessing in Colab
```python
!pip install huggingface_hub -q
from huggingface_hub import snapshot_download

repo_id = "starsofchance/MSR_data_cleaned"
dataset_path = snapshot_download(repo_id=repo_id, repo_type="dataset")
```

### 2. Extracting the Dataset
```python
!apt-get install unzip -qq
!unzip "/root/.cache/huggingface/.../MSR_data_cleaned.zip" -d "/content/extracted_data"
```
**Note:** Extracted size is **10GB** (1.5GB compressed).

### 3. Creating Splits (Colab Pro Recommended)
We used this memory-efficient approach:
```python
from datasets import load_dataset
dataset = load_dataset("csv", data_files="MSR_data_cleaned.csv", streaming=True)

# Randomly distribute rows (80-10-10)
for row in dataset:
    rand = random.random()
    if rand < 0.8: write_to(train.csv)
    elif rand < 0.9: write_to(validation.csv)
    else: write_to(test.csv)
```
**Hardware Requirements:**
- Minimum 25GB RAM
- Strong CPU (Colab Pro T4 GPU recommended)

## 📜 Citation
```bibtex
@inproceedings{fan2020ccode,
  title={A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries},
  author={Fan, Jiahao and Li, Yi and Wang, Shaohua and Nguyen, Tien N},
  booktitle={MSR '20: 17th International Conference on Mining Software Repositories},
  pages={1--5},
  year={2020},
  doi={10.1145/3379597.3387501}
}
```

## 🌟 Dataset Creation
- **Source**: Original data from [MSR 2020 Paper](https://doi.org/10.1145/3379597.3387501)
- **Processing**:
  - Cleaned and standardized CSV format
  - Stream-based splitting to handle large size
  - Preserved all original metadata