Datasets:
code_x_glue_cc_defect_detection

	---
	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: train
	num_bytes: 45723451
	num_examples: 21854
	- name: validation
	num_bytes: 5582533
	num_examples: 2732
	- name: test
	num_bytes: 5646740
	num_examples: 2732
	download_size: 22289955
	dataset_size: 56952724
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: validation
	path: data/validation-*
	- split: test
	path: data/test-*
	---
	# Dataset Card for "code_x_glue_cc_defect_detection"

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits-sample-size)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#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.