code_x_glue_cc_defect_detection

# 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.

# "code_x_glue_cc_defect_detection"数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集摘要](#dataset-summary)
- [支持的任务与排行榜](#supported-tasks)
- [编程语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分（样本量）](#data-splits-sample-size)
- [数据集创建](#dataset-creation)
- [整理依据](#curation-rationale)
- [源数据](#source-data)
- [标注](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献](#contributions)

## 数据集描述

- **主页**：https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection

### 数据集摘要

CodeXGLUE缺陷检测数据集，获取地址为https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection。给定一段源代码，任务为识别其是否为可能攻击软件系统的不安全代码，例如资源泄漏、释放后使用（use-after-free）漏洞以及拒绝服务（DoS）攻击。我们将该任务视为二分类（0/1）任务，其中1代表不安全代码，0代表安全代码。本数据集源自论文《Devign：通过图神经网络（Graph Neural Networks）学习全面程序语义实现高效漏洞识别》（Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks）。我们整合所有项目，并按照80%/10%/10%的比例划分为训练集/验证集/测试集。

### 支持的任务与排行榜

- `多分类（multi-class-classification）`：该数据集可用于训练模型以检测代码是否存在缺陷。

### 编程语言

- C语言

## 数据集结构

### 数据实例

验证集的一个示例如下：

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

### 数据字段

以下将针对各配置逐一解释数据字段，所有划分下的数据字段均保持一致。

#### 默认配置

| 字段名 | 类型 | 描述 |
| ------ | ------- | ---- |
| id | int32 | 样本索引 |
| func | string | 源代码 |
| target | bool | 0或1（分别代表存在漏洞或不存在漏洞） |
| project| string | 包含该代码的原始项目 |
| commit_id | string | 原始项目中的提交标识符 |

### 数据划分

| 名称 | 训练集 | 验证集 | 测试集 |
| ------- | -----: | ------: | -----: |
| default | 21854 | 2732 | 2732 |

## 数据集创建

### 整理依据

[需要更多信息]

### 源数据

#### 初始数据收集与归一化

[需要更多信息]

#### 源语言生产者是谁？

[需要更多信息]

### 标注

#### 标注流程

[需要更多信息]

#### 标注者是谁？

[需要更多信息]

### 个人与敏感信息

[需要更多信息]

## 数据集使用注意事项

### 数据集的社会影响

[需要更多信息]

### 偏差讨论

[需要更多信息]

### 其他已知局限性

[需要更多信息]

## 附加信息

### 数据集维护者

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

### 许可信息

计算数据使用协议（Computational Use of Data Agreement, C-UDA）许可证。

### 引用信息

@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}
}

### 贡献

感谢@madlag（部分贡献来自@ncoop57）添加此数据集。