---
license: mit
datasets:
- mahdin70/cwe_enriched_balanced_bigvul_primevul
base_model:
- microsoft/unixcoder-base
library_name: transformers
---

# UnixCoder-VulnCWE - Fine-Tuned UnixCoder for Vulnerability and CWE Classification

## Model Overview
This model is a fine-tuned version of **microsoft/unixcoder-base** on a curated and enriched dataset for vulnerability detection and CWE classification. It is capable of predicting whether a given code snippet is vulnerable and, if vulnerable, identifying the specific CWE ID associated with it.

## Dataset
The model was fine-tuned using the dataset [mahdin70/cwe_enriched_balanced_bigvul_primevul](https://huggingface.co/datasets/mahdin70/cwe_enriched_balanced_bigvul_primevul). The dataset contains both vulnerable and non-vulnerable code samples and is enriched with CWE metadata.

### CWE IDs Covered:
1. **CWE-119**: Improper Restriction of Operations within the Bounds of a Memory Buffer  
2. **CWE-20**: Improper Input Validation  
3. **CWE-125**: Out-of-bounds Read  
4. **CWE-399**: Resource Management Errors  
5. **CWE-200**: Information Exposure  
6. **CWE-787**: Out-of-bounds Write  
7. **CWE-264**: Permissions, Privileges, and Access Controls  
8. **CWE-416**: Use After Free  
9. **CWE-476**: NULL Pointer Dereference  
10. **CWE-190**: Integer Overflow or Wraparound  
11. **CWE-189**: Numeric Errors  
12. **CWE-362**: Concurrent Execution using Shared Resource with Improper Synchronization  

---

## Model Training
The model was trained for **3 epochs** with the following configuration:
- **Learning Rate**: 2e-5  
- **Weight Decay**: 0.01  
- **Batch Size**: 8  
- **Optimizer**: AdamW  
- **Scheduler**: Linear  

### Training Loss and Validation Loss Per Epoch:
| Epoch | Training Loss | Validation Loss | Vul Accuracy | Vul Precision | Vul Recall | Vul F1 | CWE Accuracy |
|------|---------------|----------------|--------------|---------------|-----------|-------|---------------|
| 1    | 1.3732        | 1.2689         | 0.8220       | 0.8831        | 0.6231    | 0.7307| 0.4032        |
| 2    | 1.0318        | 1.1613         | 0.8229       | 0.8238        | 0.6907    | 0.7514| 0.4903        |
| 3    | 0.8192        | 1.1871         | 0.8158       | 0.7997        | 0.6999    | 0.7465| 0.5326        |

#### Training Summary:
- **Total Training Steps**: 2958  
- **Training Loss**: 1.1267  
- **Training Time**: 2687.8 seconds (~45 minutes)  
- **Training Speed**: 17.6 samples per second  
- **Steps Per Second**: 1.1  

---

## Model Evaluation (Test Set Results)
The model was evaluated on the test set with the following metrics:

### Vulnerability Detection Metrics:
- **Accuracy**: 82.73%  
- **Precision**: 82.15%  
- **Recall**: 70.86%  
- **F1-Score**: 76.09%  

### CWE Classification Metrics:
- **Accuracy**: 51.46%  
- **Precision**: 51.11%  
- **Recall**: 51.46%  
- **F1-Score**: 50.65%  

---

## How to Use the Model
```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("mahdin70/UnixCoder-VulnCWE", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base")

code_snippet = "int main() { int arr[10]; arr[11] = 5; return 0; }"
inputs = tokenizer(code_snippet, return_tensors="pt")
outputs = model(**inputs)

vul_logits = outputs["vul_logits"]
cwe_logits = outputs["cwe_logits"]

vul_pred = vul_logits.argmax(dim=1).item()
cwe_pred = cwe_logits.argmax(dim=1).item()

print(f"Vulnerability: {'Vulnerable' if vul_pred == 1 else 'Non-vulnerable'}")
print(f"CWE ID: {cwe_pred if vul_pred == 1 else 'N/A'}")
```

## Limitations and Future Improvements
- The model has limited accuracy on CWE classification (51.46%). Improving the model with advanced architectures or better data balancing could yield better results.
- The model might not perform well on edge cases or unseen CWEs.