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# 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
[](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
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