README.md

metadata

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:
  - conditional-text-generation-other-debugging

Dataset Card for "code_x_glue_cc_code_refinement"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • 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

• conditional-text-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.