README.md

---
dataset_info:
  features:
  - name: conversations
    list:
    - name: from
      dtype: string
    - name: value
      dtype: string
  - name: source
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 93911454
    num_examples: 26296
  - name: test
    num_bytes: 10913928
    num_examples: 2922
  download_size: 34387789
  dataset_size: 104825382
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- code
- code vulnerability
- C
- C++
- cpp
- en
pretty_name: Code Vulnerability Balanced — CWE-Enriched Conversation Dataset
size_categories:
- 10K<n<100K
---
# Code Vulnerability Balanced — CWE-Enriched Conversation Dataset

## 📌 Overview

This dataset is a **balanced and shuffled** version of
[ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune](https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune),
which itself was derived from the original
[ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset](https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset)
(330k rows, sourced from DiverseVul + MITRE CWE enrichment).

The original fine-tuning dataset was **imbalanced** — the number of **Vulnerable** and **Safe**
samples were not equal — and the samples were **not shuffled**. This dataset addresses both issues:

- ✅ **Balanced** — Equal number of Vulnerable and Safe (patched) samples
- ✅ **Shuffled** — Samples are randomly shuffled to prevent ordering bias during training
- ✅ **ShareGPT / FineTome format** — Ready for fine-tuning with Unsloth, TRL, and similar frameworks

---

## 🎯 Use Cases

- Fine-tuning LLMs for security code review
- Training vulnerability detection models
- Building code-aware security assistants
- Research in automated static analysis and secure coding

---

## 📊 Dataset Statistics

| Property | Value |
|---|---|
| Source Dataset | ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune |
| Balancing Strategy | Undersample majority class to match minority class |
| Shuffled | Yes (random seed 42) |
| Format | ShareGPT (conversations) |
| Languages | C, C++ |
| Splits | train (90%) · test (10%) |
| License | MIT |

---

## 🔄 What Changed From the Source Dataset

| Change | Description |
|---|---|
| **Balancing** | The source dataset had an unequal number of Vulnerable vs Safe samples. This dataset undersamples the majority class so both are equal in count. |
| **Shuffling** | All rows are randomly shuffled (seed 42) before splitting, preventing the model from learning ordering patterns. |
| **Same format** | The ShareGPT conversation structure is preserved exactly as in the source dataset. |

---

## 🗂️ Data Format

Each row follows the ShareGPT conversation format with two turns:

```json
{
  "conversations": [
    {
      "from": "human",
      "value": "Analyze the following code snippet and identify any security vulnerabilities...\n\n```c\n<source code>\n```"
    },
    {
      "from": "gpt",
      "value": "## Security Vulnerability Analysis\n\n⚠️ This code sample is marked as **Vulnerable**.\n\n### 🔍 Vulnerability Classification\n- **CWE ID**: CWE-787\n- **Type**: Out-of-bounds Write\n- **Severity**: High\n..."
    }
  ],
  "source": "code_vulnerability_cwe",
  "score": 4.8
}
```

### Fields

| Field | Type | Description |
|---|---|---|
| `conversations` | list | List of 2 conversation turns |
| `conversations[0].from` | str | Always `"human"` |
| `conversations[0].value` | str | Instruction + C/C++ code block |
| `conversations[1].from` | str | Always `"gpt"` |
| `conversations[1].value` | str | Structured vulnerability analysis |
| `source` | str | Always `"code_vulnerability_cwe"` |
| `score` | float | Quality score (4.8) |

---

## 🔄 Preprocessing Pipeline

### Step 1 — Load
Load the source dataset from Hugging Face Hub
(`ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune`).

### Step 2 — Separate by Label
Split all rows into two groups:
- **Vulnerable** samples
- **Safe** (patched) samples

### Step 3 — Balance
Undersample the larger group so that both groups have an equal number of rows,
resulting in a 50/50 class distribution.

### Step 4 — Shuffle
Combine the two balanced groups and shuffle the entire dataset with a fixed
random seed (42) for reproducibility.

### Step 5 — Split & Push
- 90% / 10% train-test split (random seed 42)
- Pushed to Hugging Face Hub in Parquet format

---

## 📋 CWE Categories Covered

The dataset covers a wide range of Common Weakness Enumeration types including:

| CWE ID | Vulnerability Type |
|---|---|
| CWE-787 | Out-of-bounds Write |
| CWE-416 | Use After Free |
| CWE-125 | Out-of-bounds Read |
| CWE-476 | NULL Pointer Dereference |
| CWE-119 | Improper Buffer Restriction |
| CWE-284 | Improper Access Control |
| CWE-20 | Improper Input Validation |
| CWE-78 | OS Command Injection |
| CWE-327 | Broken Cryptographic Algorithm |
| CWE-835 | Infinite Loop |
| CWE-122 | Heap-based Buffer Overflow |
| CWE-362 | Race Condition |
| CWE-22 | Path Traversal |
| CWE-287 | Improper Authentication |
| CWE-319 | Cleartext Transmission |
| ... | 30+ more CWE types |

---

## 🧪 Sample Example

### Human Turn

```
Analyze the following code snippet and identify any security vulnerabilities.
Provide a detailed explanation of the vulnerability type, its severity,
potential impact, and the CWE classification.

static boolean ReadICCProfile(j_decompress_ptr jpeg_info) {
  char magick[12];
  ErrorManager *error_manager;
  ExceptionInfo *exception;
  Image *image;
  ...
}
```

### Assistant Turn

```markdown
## Security Vulnerability Analysis

⚠️ This code sample is marked as **Vulnerable**.

### 🔍 Vulnerability Classification
- **CWE ID**: CWE-416
- **Type**: Use After Free
- **Severity**: High
- **Category**: Memory Corruption
- **Affected Languages**: C, C++

### 📋 Description
The product reuses or references memory after it has been freed. At some point
afterward, the memory may be allocated again and saved in another pointer, while
the original pointer references a location somewhere within the new allocation.

### 💥 Potential Impact
  - Execute Unauthorized Code or Commands
  - Read Memory
  - Modify Memory
  - DoS: Crash, Exit, or Restart

### 🛡️ Recommendation
Review the code for Use After Free patterns. Ensure proper bounds checking,
input validation, and memory management practices are applied as recommended
by the CWE guidelines for CWE-416.
```

---

## 🚀 Usage

### Load with 🤗 Datasets

```python
from datasets import load_dataset

dataset = load_dataset("ChamaraVishwajithRajapaksha/Code-Vulnerability-Balanced")
print(dataset)
# DatasetDict({
#     train: Dataset({features: ['conversations', 'source', 'score'], num_rows: ...}),
#     test:  Dataset({features: ['conversations', 'source', 'score'], num_rows: ...})
# })
```

### Access a Sample

```python
sample = dataset['train'][0]

# Print the human question (code to analyze)
print(sample['conversations'][0]['value'])

# Print the assistant answer (vulnerability analysis)
print(sample['conversations'][1]['value'])
```

### Fine-tuning with Unsloth / TRL

```python
from trl import SFTTrainer
from unsloth import FastLanguageModel

# The dataset is already in ShareGPT format — compatible with
# most fine-tuning frameworks that support conversation datasets.
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset['train'],
    dataset_text_field="conversations",  # adjust per framework
    ...
)
```

---

## 📁 Dataset Lineage

```
bstee615/diversevul
    └──> ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset
              (330k rows, CWE-enriched via MITRE API)
         └──> ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune
                   (ShareGPT format, unbalanced, unshuffled)
              └──> ChamaraVishwajithRajapaksha/Code-Vulnerability-Balanced
                        (balanced + shuffled — this dataset)
```

---

## ⚠️ Limitations

- Code samples are primarily in C and C++ — limited coverage of other languages
- Balancing is achieved by **undersampling** the majority class, so total row count is reduced compared to the source dataset
- The Safe samples represent patched/fixed versions, not inherently safe code — context matters
- CWE details describe the class of vulnerability, not a precise analysis of each individual function
- This dataset is intended for research and educational purposes

---

## 📜 License

This dataset is released under the **MIT License**, consistent with the source dataset license.

---

## 🙏 Citation

If you use this dataset in your research, please cite the original source and this dataset:

```bibtex
@dataset{code_vulnerability_balanced,
  title        = {Code Vulnerability Balanced: CWE-Enriched Conversation Dataset},
  author       = {ChamaraVishwajithRajapaksha},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-Balanced},
  note         = {Balanced and shuffled version of Code-Vulnerability-FineTune, in ShareGPT format}
}
```

---

## 🔗 Related Resources

- [MITRE CWE Database](https://cwe.mitre.org/)
- [DiverseVul Paper](https://github.com/bstee615/diversevul)
- [Source: Code-Vulnerability-FineTune](https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune)
- [FineTome-100k Format Reference](https://huggingface.co/datasets/mlabonne/FineTome-100k)
- [Unsloth Fine-tuning](https://github.com/unslothai/unsloth)
- [TRL SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)