Datasets:

google
/

code_x_glue_cc_code_refinement

	---
	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:
	- text2text-generation
	task_ids:
	- text2text-generation-other-debugging
	pretty_name: CodeXGlueCcCodeRefinement
	---

	# 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

	### 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{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.