arxiv: 1807.0432
dataset_info:
features:
- name: functionSource
dtype: string
- name: CWE-119
dtype: bool
- name: CWE-120
dtype: bool
- name: CWE-469
dtype: bool
- name: CWE-476
dtype: bool
- name: CWE-other
dtype: bool
- name: combine
dtype: int64
splits:
- name: train
num_bytes: 832092463
num_examples: 1019471
- name: validation
num_bytes: 104260416
num_examples: 127476
- name: test
num_bytes: 104097361
num_examples: 127419
download_size: 535360739
dataset_size: 1040450240
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
task_categories:
- text-classification
tags:
- code
This is an unofficial HuggingFace version of "Draper VDISC Dataset - Vulnerability Detection in Source Code" dataset from "Automated Vulnerability Detection in Source Code Using Deep Representation Learning".
Draper VDISC Dataset - Vulnerability Detection in Source Code
The dataset consists of the source code of 1.27 million functions mined from open source software, labeled by static analysis for potential vulnerabilities. For more details on the dataset and benchmark results, see https://arxiv.org/abs/1807.04320.
The data is provided in three HDF5 files corresponding to an 80:10:10 train/validate/test split, matching the splits used in our paper. The combined file size is roughly 1 GB. Each function's raw source code, starting from the function name, is stored as a variable-length UTF-8 string. Five binary 'vulnerability' labels are provided for each function, corresponding to the four most common CWEs in our data plus all others:
CWE-120 (3.7% of functions)
CWE-119 (1.9% of functions)
CWE-469 (0.95% of functions)
CWE-476 (0.21% of functions)
CWE-other (2.7% of functions)
Functions may have more than one detected CWE each.
Please cite our paper if you use this dataset in a publication: https://arxiv.org/abs/1807.04320
This project was sponsored by the Air Force Research Laboratory (AFRL) as part of the DARPA MUSE (https://www.darpa.mil/program/mining-and-understanding-software-enclaves) program.
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