# 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.
task_categories:text-classification task_ids:semantic-similarity-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:code license:c-uda region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["code"], "license": ["c-uda"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "CodeXGlueCcCloneDetectionBigCloneBench", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "id1", "dtype": "int32"}, {"name": "id2", "dtype": "int32"}, {"name": "func1", "dtype": "string"}, {"name": "func2", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2888035029, "num_examples": 901028}, {"name": "validation", "num_bytes": 1371399358, "num_examples": 415416}, {"name": "test", "num_bytes": 1220662565, "num_examples": 415416}], "download_size": 1279275281, "dataset_size": 5480096952}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
TAGS
#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-code #license-c-uda #region-us 

Dataset Card for "code\_x\_glue\_cc\_clone\_detection\_big\_clone\_bench"
=========================================================================


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: URL


### Dataset Summary


CodeXGLUE Clone-detection-BigCloneBench dataset, available at URL


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.


### 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: id, type: int32, description: Index of the sample
field name: id1, type: int32, description: The first function id
field name: id2, type: int32, description: The second function id
field name: func1, type: string, description: The full text of the first function
field name: func2, type: string, description: The full text of the second function
field name: label, type: bool, description: 1 is the functions are not equivalent, 0 otherwise


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


Data was mined from the IJaDataset 2.0 dataset.


#### Who are the source language producers?


### Annotations


#### Annotation process


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


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


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


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


URL URL


### Licensing Information


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


### Contributions


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



### Dataset Summary


CodeXGLUE Clone-detection-BigCloneBench dataset, available at URL


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. ### 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: id, type: int32, description: Index of the sample
field name: id1, type: int32, description: The first function id
field name: id2, type: int32, description: The second function id
field name: func1, type: string, description: The full text of the first function
field name: func2, type: string, description: The full text of the second function
field name: label, type: bool, description: 1 is the functions are not equivalent, 0 otherwise ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


Data was mined from the IJaDataset 2.0 dataset. #### Who are the source language producers? ### Annotations #### Annotation process


Data was manually labeled by three judges by automatically identifying potential clones using search heuristics. #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases


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


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


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


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-code #license-c-uda #region-us 
 ### Dataset Summary


CodeXGLUE Clone-detection-BigCloneBench dataset, available at URL


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. ### 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: id, type: int32, description: Index of the sample
field name: id1, type: int32, description: The first function id
field name: id2, type: int32, description: The second function id
field name: func1, type: string, description: The full text of the first function
field name: func2, type: string, description: The full text of the second function
field name: label, type: bool, description: 1 is the functions are not equivalent, 0 otherwise ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


Data was mined from the IJaDataset 2.0 dataset. #### Who are the source language producers? ### Annotations #### Annotation process


Data was manually labeled by three judges by automatically identifying potential clones using search heuristics. #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases


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


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


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


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-code #license-c-uda #region-us 
### Dataset Summary


CodeXGLUE Clone-detection-BigCloneBench dataset, available at URL


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.### 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: id, type: int32, description: Index of the sample
field name: id1, type: int32, description: The first function id
field name: id2, type: int32, description: The second function id
field name: func1, type: string, description: The full text of the first function
field name: func2, type: string, description: The full text of the second function
field name: label, type: bool, description: 1 is the functions are not equivalent, 0 otherwise### Data Splits



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization


Data was mined from the IJaDataset 2.0 dataset.#### Who are the source language producers?### Annotations