README.md

metadata

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 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

+---------------+-----------------+----------------------+---------------------------+
| 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

!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

!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:

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

@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
• Processing:
  • Cleaned and standardized CSV format
  • Stream-based splitting to handle large size
  • Preserved all original metadata