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Datasets:
code_x_glue_cc_code_refinement

Tasks:
Text2Text Generation
Languages: code
Multilinguality: other-programming-languages
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: original
ArXiv:
Tags: debugging
License:
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metadata
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 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.