Datasets:
code_x_glue_cc_code_refinement

	---
	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: 32614786
	num_examples: 52364
	- name: validation
	num_bytes: 4086733
	num_examples: 6546
	- name: test
	num_bytes: 4063665
	num_examples: 6545
	download_size: 14929559
	dataset_size: 40765184
	- config_name: small
	features:
	- name: id
	dtype: int32
	- name: buggy
	dtype: string
	- name: fixed
	dtype: string
	splits:
	- name: train
	num_bytes: 13006679
	num_examples: 46680
	- name: validation
	num_bytes: 1629242
	num_examples: 5835
	- name: test
	num_bytes: 1619700
	num_examples: 5835
	download_size: 5894462
	dataset_size: 16255621
	configs:
	- config_name: medium
	data_files:
	- split: train
	path: medium/train-*
	- split: validation
	path: medium/validation-*
	- split: test
	path: medium/test-*
	- config_name: small
	data_files:
	- split: train
	path: small/train-*
	- split: validation
	path: small/validation-*
	- split: test
	path: small/test-*
	---

	# Dataset Card for "code_x_glue_cc_code_refinement"

	## 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-refinement
	- Paper: https://arxiv.org/abs/2102.04664

	### 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.