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.