Datasets:

google
/

code_x_glue_cc_clone_detection_big_clone_bench

	---
	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-*
	---
	# 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.