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---
dataset_info:
features:
- name: project
dtype: string
- name: commit_id
dtype: string
- name: func
dtype: string
- name: vul
dtype: int64
- name: CVE ID
dtype: string
- name: CWE ID
dtype: string
- name: CWE Name
dtype: string
- name: CWE Description
dtype: string
- name: Potential Mitigation
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 64041861
num_examples: 15770
- name: validation
num_bytes: 9198025
num_examples: 2253
- name: test
num_bytes: 17932135
num_examples: 4506
download_size: 25107430
dataset_size: 91172021
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# CWE Enriched and Balanced BigVul+PrimeVul
This dataset is a processed and enriched version of the **BigVul and PrimeVul datasets**, enhanced using CWE mappings from [MITRE's CWE Database](https://cwe.mitre.org/). The purpose of this dataset is to provide a cleaner, more balanced, and more informative version of the original datasets for vulnerability prediction and CWE classification tasks.
## 📌 Dataset Description
This dataset contains vulnerability information from BigVul and PrimeVul datasets, with additional columns added from CWE ID mappings fetched from MITRE’s CWE database. The dataset was cleaned, balanced, and split into **training, validation, and test sets** while maintaining:
- The distribution ratio of vulnerable and non-vulnerable functions.
- The distribution ratio of each CWE ID across all splits.
### 💡 Key Features:
- **Project & Commit Information:** Metadata related to the project and its commits.
- **Function Code (`func`):** The code snippet associated with each entry.
- **Vulnerability Label (`vul`):** Whether the function is vulnerable (`1`) or not (`0`).
- **CVE ID & CWE ID Mapping:** Relevant IDs for categorizing vulnerabilities.
- **CWE Information:** Additional information fetched from MITRE's CWE database including:
- `CWE Name`
- `CWE Description`
- `Potential Mitigation`
## 📂 Dataset Structure
The dataset consists of the following columns:
| Column Name | Description |
|-----------------------|----------------------------------------------------------------|
| project | The name of the project where the code is taken from. |
| commit_id | The unique commit identifier. |
| func | The function code snippet. |
| vul | Label indicating if the function is vulnerable (`1`) or not (`0`). |
| CVE ID | The CVE identifier (if applicable). |
| CWE ID | The CWE identifier corresponding to the vulnerability. |
| CWE Name | The name of the CWE ID. |
| CWE Description | Description of the CWE category. |
| Potential Mitigation | Recommended mitigations for addressing the vulnerability. |
## 📊 CWE IDs Contained In This Dataset
The dataset contains the following **13 unique CWE IDs (Including Not Applicable)**:
| CWE ID | CWE Name |
|-----------|-------------------------------------|
| CWE-119 | Improper Restriction of Operations within the Bounds of a Memory Buffer |
| CWE-20 | Improper Input Validation |
| CWE-125 | Out-of-bounds Read |
| CWE-399 | Resource Management Errors |
| CWE-200 | Information Exposure |
| CWE-787 | Out-of-bounds Write |
| CWE-264 | Permissions, Privileges, and Access Control |
| CWE-416 | Use After Free |
| CWE-476 | NULL Pointer Dereference |
| CWE-190 | Integer Overflow or Wraparound |
| CWE-189 | Numeric Errors |
| CWE-362 | Concurrent Execution using Shared Resource with Improper Synchronization (Race Condition) |
| NOT_APPLICABLE | Not applicable / No CWE ID assigned |
## 🔍 Data Splitting & Balancing
The dataset has been split into **training, validation, and test sets** using stratified sampling to ensure the distribution of:
1. Vulnerable (`vul = 1`) and Non-Vulnerable (`vul = 0`) functions.
2. Each CWE ID remains consistent across splits.
## 📥 Usage
To load this dataset from the Hugging Face Hub:
```python
from datasets import load_dataset
dataset = load_dataset('mahdin70/cwe_enriched_balanced_bigvul_primevul')
```
## 📌 Citation
If you use this dataset, please cite the original sources of BigVul and PrimeVul datasets. |