---
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: func
    dtype: string
  - name: target
    dtype: bool
  - name: project
    dtype: string
  - name: commit_id
    dtype: string
  - name: func_clean
    dtype: string
  - name: vul_lines
    struct:
    - name: code
      sequence: string
    - name: line_no
      sequence: int64
  - name: normalized_func
    dtype: string
  - name: lines
    sequence: string
  - name: label
    sequence: int64
  - name: line_no
    sequence:
      sequence: int64
  splits:
  - name: test
    num_bytes: 22801956
    num_examples: 2732
  - name: train
    num_bytes: 183794878
    num_examples: 21854
  - name: validation
    num_bytes: 22451009
    num_examples: 2732
  download_size: 72100845
  dataset_size: 229047843
---
# Dataset Card for "devign_with_norm_vul_lines"

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)


Original Paper: https://www.sciencedirect.com/science/article/abs/pii/S0167739X24004680

bibtex

```
@article{TRAN2024107504,
title = {DetectVul: A statement-level code vulnerability detection for Python},
journal = {Future Generation Computer Systems},
pages = {107504},
year = {2024},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2024.107504},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24004680},
author = {Hoai-Chau Tran and Anh-Duy Tran and Kim-Hung Le},
keywords = {Source code vulnerability detection, Deep learning, Natural language processing},
abstract = {Detecting vulnerabilities in source code using graph neural networks (GNN) has gained significant attention in recent years. However, the detection performance of these approaches relies highly on the graph structure, and constructing meaningful graphs is expensive. Moreover, they often operate at a coarse level of granularity (such as function-level), which limits their applicability to other scripting languages like Python and their effectiveness in identifying vulnerabilities. To address these limitations, we propose DetectVul, a new approach that accurately detects vulnerable patterns in Python source code at the statement level. DetectVul applies self-attention to directly learn patterns and interactions between statements in a raw Python function; thus, it eliminates the complicated graph extraction process without sacrificing model performance. In addition, the information about each type of statement is also leveraged to enhance the model’s detection accuracy. In our experiments, we used two datasets, CVEFixes and Vudenc, with 211,317 Python statements in 21,571 functions from real-world projects on GitHub, covering seven vulnerability types. Our experiments show that DetectVul outperforms GNN-based models using control flow graphs, achieving the best F1 score of 74.47%, which is 25.45% and 18.05% higher than the best GCN and GAT models, respectively.}
}
```