--- # YAML Metadata Block language: - en tags: - vulnerability-detection - cve - code-changes - software-security - stratified-split license: mit dataset_info: features: # Features in the *final split files* - name: idx dtype: int64 - name: func_before dtype: string - name: Vulnerability Classification dtype: string - name: vul dtype: int64 - name: func_after dtype: string - name: patch dtype: string - name: CWE ID dtype: string - name: lines_before dtype: string - name: lines_after dtype: string splits: - name: train num_examples: 150909 - name: validation num_examples: 18864 - name: test num_examples: 18863 dataset_original_file_size: 10GB uuncompressed --- # 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 from original file ```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..." | +---------------+-----------------+----------------------+---------------------------+ ``` Note: This is a simplified preview; the full dataset includes additional fields like commit_id, func_before, etc. ### 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). Ensure sufficient disk space. ### 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) ##📊 Dataset Statistics - Number of Rows: 188,636 - Vulnerability Distribution: - Vulnerable (1): 18,863 (~10%) - Non-Vulnerable (0): 169,773 (~90%) ##📋 Data Fields Description - CVE_ID: Unique identifier for the vulnerability (Common Vulnerabilities and Exposures). - CWE_ID: Weakness category identifier (Common Weakness Enumeration). - Score: CVSS score indicating severity (float, 0-10). - Summary: Brief description of the vulnerability. - commit_id: Git commit hash linked to the code change. - codeLink: URL to the code repository or commit. - file_name: Name of the file containing the vulnerability. - func_after: Function code after the change. - lines_after: Code lines after the change. - Access_Gained: Type of access gained by exploiting the vulnerability. - Attack_Origin: Source of the attack (e.g., Remote, Local). - Authentication_Required: Whether authentication is needed to exploit. - Availability: Impact on system availability. - CVE_Page: URL to the CVE details page. - Complexity: Complexity of exploiting the vulnerability. - Confidentiality: Impact on data confidentiality. - Integrity: Impact on data integrity. - Known_Exploits: Details of known exploits, if any. - Publish_Date: Date the vulnerability was published. - Update_Date: Date of the last update to the vulnerability data. - Vulnerability_Classification: Type or category of the vulnerability. - add_lines: Lines added in the commit. - del_lines: Lines deleted in the commit. - commit_message: Description of the commit. - files_changed: List of files modified in the commit. - func_before: Function code before the change. - lang: Programming language (e.g., C, C++). - lines_before: Code lines before the change. ## splits file for UltiVul project: ## 🔍 Sample Data Structure (from train.csv) ```python { 'idx': 0, # Unique ID within the train split 'func_before': '...', # String containing function code before change 'Vulnerability Classification': '...', # Original vulnerability type classification 'vul': 0, # Integer: 0 for non-vulnerable, 1 for vulnerable (target label) 'func_after': '...', # String containing function code after change 'patch': '...', # String containing diff patch 'CWE ID': '...', # String CWE ID, e.g., "CWE-119" 'lines_before': '...', # String lines before change context 'lines_after': '...' # String lines after change context } ``` **Note: This shows the structure of the final split files (train.csv, validation.csv, test.csv). The original MSR_data_cleaned.csv contains many more metadata fields. ##📦 Dataset New Files The dataset is available as three CSV files (specially created for the UltiVul project) hosted on Hugging Face, uploaded via huggingface_hub: - train.csv Size: 667 MB Description: Training split with 150,909 samples, approximately 80% of the data. - validation.csv Size: 86 MB Description: Validation split with 18,864 samples, approximately 10% of the data. - test.csv Size: 84.8 MB Description: Test split with 18,863 samples, approximately 10% of the data. 🙏 Acknowledgements Original dataset provided by Fan et al., 2020 Thanks to the Hugging Face team for dataset hosting tools. ## 📜 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