task_categories:translation annotations_creators:expert-generated language_creators:found multilinguality:other-programming-languages size_categories:10K<n<100K source_datasets:original language:code license:c-uda code-to-code arxiv:2102.04664 region:us
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.
@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}
}
---
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: 4372657
    num_examples: 10300
  - name: validation
    num_bytes: 226415
    num_examples: 500
  - name: test
    num_bytes: 418595
    num_examples: 1000
  download_size: 4876035
  dataset_size: 5017667
---
# 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.