Datasets:
code_x_glue_cc_code_to_code_trans

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- found
	language:
	- code
	license:
	- c-uda
	multilinguality:
	- other-programming-languages
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- translation
	task_ids: []
	pretty_name: CodeXGlueCcCodeToCodeTrans
	tags:
	- code-to-code
	dataset_info:
	features:
	- name: id
	dtype: int32
	- name: java
	dtype: string
	- name: cs
	dtype: string
	splits:
	- name: train
	num_bytes: 4372641
	num_examples: 10300
	- name: validation
	num_bytes: 226407
	num_examples: 500
	- name: test
	num_bytes: 418587
	num_examples: 1000
	download_size: 2064764
	dataset_size: 5017635
	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_code_to_code_trans"

	## 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/code-to-code-trans
	- Paper: https://arxiv.org/abs/2102.04664

	### Dataset Summary

	CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans

	The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).

	We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.

	### Supported Tasks and Leaderboards

	- `machine-translation`: The dataset can be used to train a model for translating code in Java to C# and vice versa.

	### Languages

	- Java programming language
	- C# programming language

	## Dataset Structure

	### Data Instances

	An example of 'validation' looks as follows.
	```
	{
	"cs": "public DVRecord(RecordInputStream in1){_option_flags = in1.ReadInt();_promptTitle = ReadUnicodeString(in1);_errorTitle = ReadUnicodeString(in1);_promptText = ReadUnicodeString(in1);_errorText = ReadUnicodeString(in1);int field_size_first_formula = in1.ReadUShort();_not_used_1 = in1.ReadShort();_formula1 = NPOI.SS.Formula.Formula.Read(field_size_first_formula, in1);int field_size_sec_formula = in1.ReadUShort();_not_used_2 = in1.ReadShort();_formula2 = NPOI.SS.Formula.Formula.Read(field_size_sec_formula, in1);_regions = new CellRangeAddressList(in1);}\n",
	"id": 0,
	"java": "public DVRecord(RecordInputStream in) {_option_flags = in.readInt();_promptTitle = readUnicodeString(in);_errorTitle = readUnicodeString(in);_promptText = readUnicodeString(in);_errorText = readUnicodeString(in);int field_size_first_formula = in.readUShort();_not_used_1 = in.readShort();_formula1 = Formula.read(field_size_first_formula, in);int field_size_sec_formula = in.readUShort();_not_used_2 = in.readShort();_formula2 = Formula.read(field_size_sec_formula, in);_regions = new CellRangeAddressList(in);}\n"
	}
	```

	### 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 \|
	\|java \|string\| The java version of the code\|
	\|cs \|string\| The C# version of the code \|

	### Data Splits

	\| name \|train\|validation\|test\|
	\|-------\|----:\|---------:\|---:\|
	\|default\|10300\| 500\|1000\|

	## 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

	```
	@article{DBLP:journals/corr/abs-2102-04664,
	author = {Shuai Lu and
	Daya Guo and
	Shuo Ren and
	Junjie Huang and
	Alexey Svyatkovskiy and
	Ambrosio Blanco and
	Colin B. Clement and
	Dawn Drain and
	Daxin Jiang and
	Duyu Tang and
	Ge Li and
	Lidong Zhou and
	Linjun Shou and
	Long Zhou and
	Michele Tufano and
	Ming Gong and
	Ming Zhou and
	Nan Duan and
	Neel Sundaresan and
	Shao Kun Deng and
	Shengyu Fu and
	Shujie Liu},
	title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
	and Generation},
	journal = {CoRR},
	volume = {abs/2102.04664},
	year = {2021}
	}
	```

	### Contributions

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