dataset_info:
dataset_name: LineVul Dataset Splits
description: >
This dataset contains splits (train, validation, test) of the LineVul
dataset, originally designed for transformer-based line-level vulnerability
prediction in C/C++ code. It includes processed functions, vulnerability
labels, and fixed versions of vulnerable functions.
version: 1.0.0
license: MIT
splits:
- name: train
description: Training split of the LineVul dataset.
- name: validation
description: Validation split of the LineVul dataset.
- name: test
description: Test split of the LineVul dataset.
features:
- name: processed_func
dtype: string
description: The original function written in C/C++.
- name: target
dtype: int
description: Function-level label (1 for vulnerable, 0 for non-vulnerable).
- name: vul_func_with_fix
dtype: string
description: The fixed function with added/deleted lines labeled.
LineVul Dataset Splits
This dataset provides the train, validation, and test splits of the LineVul dataset, originally introduced in the paper "LineVul: A Transformer-based Line-Level Vulnerability Prediction" by Michael Fu and Chakkrit Tantithamthavorn. The dataset is designed for predicting software vulnerabilities at the line level in C/C++ code using transformer-based models. It was sourced from the LineVul replication package available at https://github.com/awsm-research/LineVul.
Dataset Description
The LineVul dataset consists of C/C++ functions with associated vulnerability labels and fixed versions. Each split (train, validation, test) contains 39 columns, but the key columns used for vulnerability prediction are:
- processed_func (string): The original function written in C/C++.
- target (int): A binary label indicating whether the function is vulnerable (1) or not (0).
- vul_func_with_fix (string): The fixed version of the function, with added and deleted lines labeled.
The dataset is intended for training and evaluating models that predict vulnerabilities in software code, particularly at the line level.
Citation
The original dataset and methodology are detailed in the following paper:
@inproceedings{fu2022linevul,
title={LineVul: A Transformer-based Line-Level Vulnerability Prediction},
author={Fu, Michael and Tantithamthavorn, Chakkrit},
booktitle={2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)},
year={2022},
organization={IEEE}
}
Please cite this paper if you use this dataset in your research.
License
The dataset is released under the MIT License, consistent with the original LineVul replication package.