Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- code
licenses:
- other-C-UDA
multilinguality:
- other-programming-languages
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
Dataset Card for "code_x_glue_cc_code_to_code_trans"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
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{CodeXGLUE,
title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
year={2020},}
Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.