code_x_glue_cc_clone_detection_big_clone_bench

# Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"

## 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/Clone-detection-BigCloneBench

### Dataset Summary

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

Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.

### Supported Tasks and Leaderboards

- `semantic-similarity-classification`: The dataset can be used to train a model for classifying if two given java methods are cloens of each other.

### Languages

- Java **programming** language

## Dataset Structure

### Data Instances

An example of 'test' looks as follows.
```
{
"func1": " @Test(expected = GadgetException.class)\n public void malformedGadgetSpecIsCachedAndThrows() throws Exception {\n HttpRequest request = createCacheableRequest();\n expect(pipeline.execute(request)).andReturn(new HttpResponse(\"malformed junk\")).once();\n replay(pipeline);\n try {\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n fail(\"No exception thrown on bad parse\");\n } catch (GadgetException e) {\n }\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n }\n",
"func2": " public InputStream getInputStream() throws TGBrowserException {\n try {\n if (!this.isFolder()) {\n URL url = new URL(this.url);\n InputStream stream = url.openStream();\n return stream;\n }\n } catch (Throwable throwable) {\n throw new TGBrowserException(throwable);\n }\n return null;\n }\n",
"id": 0,
"id1": 2381663,
"id2": 4458076,
"label": false
}
```

### 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 |
|id1 |int32 | The first function id |
|id2 |int32 | The second function id |
|func1 |string| The full text of the first function |
|func2 |string| The full text of the second function |
|label |bool | 1 is the functions are not equivalent, 0 otherwise|

### Data Splits

| name |train |validation| test |
|-------|-----:|---------:|-----:|
|default|901028| 415416|415416|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

Data was mined from the IJaDataset 2.0 dataset.
[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

Data was manually labeled by three judges by automatically identifying potential clones using search heuristics.
[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

Most of the clones are type 1 and 2 with type 3 and especially type 4 being rare.

[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{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={476--480},
year={2014},
organization={IEEE}
}

@inproceedings{wang2020detecting,
title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={261--271},
year={2020},
organization={IEEE}
}
```

### Contributions

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

# 「code_x_glue_cc_clone_detection_big_clone_bench」数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与评测基准](#supported-tasks)
- [使用语言](#languages)
- [数据集结构](#dataset-structure)
- [数据样例](#data-instances)
- [数据字段](#data-fields)
- [数据划分（样本量）](#data-splits-sample-size)
- [数据集构建](#dataset-creation)
- [构建初衷](#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)
- [数据集社会影响](#social-impact-of-dataset)
- [偏倚讨论](#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)
- [贡献致谢](#contributions)

## 数据集描述

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

### 数据集概述

CodeXGLUE 代码克隆检测-BigCloneBench 数据集，可在 https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench 获取。本任务以两段代码作为输入，完成二分类（0/1）：1 代表两段代码语义等价，0 代表其他情况。模型性能通过 F1 分数进行评估。本数据集采用 BigCloneBench，并按照《Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree》一文的方法进行过滤。

### 支持任务与评测基准

- `语义相似度分类（semantic-similarity-classification）`：该数据集可用于训练模型，以分类两段给定的 Java 方法是否互为代码克隆（code clone）。

### 使用语言

- Java 编程语言

## 数据集结构

### 数据样例

「测试集（test）」的一条样例如下：

{
"func1": " @Test(expected = GadgetException.class)
public void malformedGadgetSpecIsCachedAndThrows() throws Exception {
HttpRequest request = createCacheableRequest();
expect(pipeline.execute(request)).andReturn(new HttpResponse("malformed junk")).once();
replay(pipeline);
try {
specFactory.getGadgetSpec(createContext(SPEC_URL, false));
fail("No exception thrown on bad parse");
} catch (GadgetException e) {
}
specFactory.getGadgetSpec(createContext(SPEC_URL, false));
}
",
"func2": " public InputStream getInputStream() throws TGBrowserException {
try {
if (!this.isFolder()) {
URL url = new URL(this.url);
InputStream stream = url.openStream();
return stream;
}
} catch (Throwable throwable) {
throw new TGBrowserException(throwable);
}
return null;
}
",
"id": 0,
"id1": 2381663,
"id2": 4458076,
"label": false
}

### 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 |
| id1 | int32 | 第一个函数的ID |
| id2 | int32 | 第二个函数的ID |
| func1 | string | 第一个函数的完整文本 |
| func2 | string | 第二个函数的完整文本 |
| label | bool | 1 代表两段函数不等价，0 代表等价 |

### 数据划分

| 划分名称 | 训练集 | 验证集 | 测试集 |
|----------|---------:|---------:|---------:|
| 默认配置 | 901028 | 415416 | 415416 |

## 数据集构建

### 构建初衷

[需要更多信息]

### 源数据

#### 初始数据收集与标准化

数据从 IJaDataset 2.0 数据集挖掘得到。[需要更多信息]

#### 源代码的创作者为谁？

[需要更多信息]

### 标注

#### 标注流程

通过搜索启发式算法自动识别潜在代码克隆后，由三名评审人员手动标注该数据集。[需要更多信息]

#### 标注人员为谁？

[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

### 数据集社会影响

[需要更多信息]

### 偏倚讨论

绝大多数克隆为类型1和类型2，类型3尤其是类型4的克隆较为罕见。[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

数据使用协议为 Computational Use of Data Agreement (C-UDA) License。

### 引用信息

@inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={476--480},
year={2014},
organization={IEEE}
}

@inproceedings{wang2020detecting,
title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={261--271},
year={2020},
organization={IEEE}
}

### 贡献致谢

感谢 @madlag（部分感谢 @ncoop57）添加此数据集。