README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - code
license:
  - c-uda
multilinguality:
  - other-programming-languages
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
pretty_name: CodeXGlueCcDefectDetection
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: func
      dtype: string
    - name: target
      dtype: bool
    - name: project
      dtype: string
    - name: commit_id
      dtype: string
  splits:
    - name: test
      num_bytes: 5646752
      num_examples: 2732
    - name: train
      num_bytes: 45723487
      num_examples: 21854
    - name: validation
      num_bytes: 5582545
      num_examples: 2732
  download_size: 61685715
  dataset_size: 56952784

Dataset Card for "code_x_glue_cc_defect_detection"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection

Dataset Summary

CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection

Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code. The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.

Supported Tasks and Leaderboards

• multi-class-classification: The dataset can be used to train a model for detecting if code has a defect in it.

Languages

• C programming language

Dataset Structure

Data Instances

An example of 'validation' looks as follows.

{
    "commit_id": "aa1530dec499f7525d2ccaa0e3a876dc8089ed1e", 
    "func": "static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n    MirrorState *s = FILTER_MIRROR(nf);\n    Chardev *chr;\n    chr = qemu_chr_find(s->outdev);\n    if (chr == NULL) {\n        error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n                  \"Device '%s' not found\", s->outdev);\n    qemu_chr_fe_init(&s->chr_out, chr, errp);", 
    "id": 8, 
    "project": "qemu", 
    "target": true
}

Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

default

field name	type	description
id	int32	Index of the sample
func	string	The source code
target	bool	0 or 1 (vulnerability or not)
project	string	Original project that contains this code
commit_id	string	Commit identifier in the original project

Data Splits

name	train	validation	test
default	21854	2732	2732

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

https://github.com/microsoft, https://github.com/madlag

Licensing Information

Computational Use of Data Agreement (C-UDA) License.

Citation Information

@inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={10197--10207}, year={2019}

Contributions

Thanks to @madlag (and partly also @ncoop57) for adding this dataset.