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:
- text2text-generation
task_ids: []
pretty_name: CodeXGlueCcCodeRefinement
tags:
- debugging
dataset_info:
- config_name: medium
features:
- name: id
dtype: int32
- name: buggy
dtype: string
- name: fixed
dtype: string
splits:
- name: train
num_bytes: 32614834
num_examples: 52364
- name: validation
num_bytes: 4086741
num_examples: 6546
- name: test
num_bytes: 4063673
num_examples: 6545
download_size: 39979724
dataset_size: 40765248
- config_name: small
features:
- name: id
dtype: int32
- name: buggy
dtype: string
- name: fixed
dtype: string
splits:
- name: train
num_bytes: 13006719
num_examples: 46680
- name: validation
num_bytes: 1629250
num_examples: 5835
- name: test
num_bytes: 1619708
num_examples: 5835
download_size: 15555421
dataset_size: 16255677
Dataset Card for "code_x_glue_cc_code_refinement"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement
We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.
Supported Tasks and Leaderboards
text2text-generation-other-debugging: The dataset can be used to train a model for automatically fixing buggy code.
Languages
- Java programming language
Dataset Structure
Data Instances
medium
An example of 'train' looks as follows.
{
"buggy": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n",
"fixed": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = null ; if ( date != null ) { VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; } VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n",
"id": 0
}
small
An example of 'validation' looks as follows.
{
"buggy": "public java.util.List < TYPE_1 > METHOD_1 ( ) { java.util.ArrayList < TYPE_1 > VAR_1 = new java.util.ArrayList < TYPE_1 > ( ) ; for ( TYPE_2 VAR_2 : VAR_3 ) { VAR_1 . METHOD_2 ( VAR_2 . METHOD_1 ( ) ) ; } return VAR_1 ; } \n",
"fixed": "public java.util.List < TYPE_1 > METHOD_1 ( ) { return VAR_1 ; } \n",
"id": 0
}
Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
medium, small
| field name | type | description |
|---|---|---|
| id | int32 | Index of the sample |
| buggy | string | The buggy version of the code |
| fixed | string | The correct version of the code |
Data Splits
| name | train | validation | test |
|---|---|---|---|
| medium | 52364 | 6546 | 6545 |
| small | 46680 | 5835 | 5835 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
Downloaded from GitHub Archive every public GitHub event between March 2011 and October 2017 and used the Google BigQuery APIs. [More Information Needed]
Who are the source language producers?
Software Engineering developers.
Annotations
Annotation process
Automatically annotated by filtering commit messages containing the pattern: ("fix" or "solve") and ("bug" or "issue" or "problem" or "error"). A statistically significant amount of samples (95% confidence level with 5% confidence interval) were manually evaluated by two authors to check if the filtered bug/fix pairs were correct. After all disagreements were settled, authors conclude that 97.6% were true positives.
Who are the annotators?
Heuristics and the authors of the paper.
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}
}
@article{tufano2019empirical,
title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
volume={28},
number={4},
pages={1--29},
year={2019},
publisher={ACM New York, NY, USA}
}
Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.