
# Dataset Card for CoCoNuT-C(2005)

## Dataset Description

- **Homepage:** [CoCoNuT training data](https://github.com/lin-tan/CoCoNut-Artifact/releases/tag/training_data_1.0.0)
- **Repository:** [CoCoNuT repository](https://github.com/lin-tan/CoCoNut-Artifact)
- **Paper:** [CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair](https://dl.acm.org/doi/abs/10.1145/3395363.3397369)

### Dataset Summary

Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset.

### Languages

- C

## Dataset Structure

### Data Fields

The dataset consists of 4 columns: `add`, `rem`, `context`, and `meta`.
These match the original dataset files: `add.txt`, `rem.txt`, `context.txt`, and `meta.txt`.

### Data Instances

There is a mapping between the 4 columns for each instance.
For example:

5 first rows of `rem` (i.e., the buggy line/hunk):

```
1 public synchronized StringBuffer append(char ch)
2 ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
3 public String substring(int beginIndex, int endIndex)
4 if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex;  return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
5 public Object next() {
```

5 first rows of add (i.e., the fixed line/hunk):

```
1 public StringBuffer append(Object obj)
2 return append(obj == null ? "null" : obj.toString());
3 public String substring(int begin)
4 return substring(begin, count);
5 public FSEntry next() {
```

These map to the 5 instances:

```diff
- public synchronized StringBuffer append(char ch)
+ public StringBuffer append(Object obj)
```

```diff
- ensureCapacity_unsynchronized(count + 1); value[count++] = ch; return this;
+ return append(obj == null ? "null" : obj.toString());
```

```diff
- public String substring(int beginIndex, int endIndex)
+ public String substring(int begin)
```

```diff
- if (beginIndex < 0 || endIndex > count || beginIndex > endIndex) throw new StringIndexOutOfBoundsException(); if (beginIndex == 0 && endIndex == count) return this; int len = endIndex - beginIndex;  return new String(value, beginIndex + offset, len, (len << 2) >= value.length);
+ return substring(begin, count);
```

```diff
- public Object next() {
+ public FSEntry next() { 
```

`context` contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of 

```
public synchronized StringBuffer append(char ch)
```

is its associated function:

```java
public synchronized StringBuffer append(char ch)  {    ensureCapacity_unsynchronized(count + 1);    value[count++] = ch;    return this;  }
```

`meta` contains some metadata about the project:

```
1056	/local/tlutelli/issta_data/temp/all_java0context/java/2006_temp/2006/1056/68a6301301378680519f2b146daec37812a1bc22/StringBuffer.java/buggy/core/src/classpath/java/java/lang/StringBuffer.java
```

`1056` is the project id. `/local/...` is the absolute path to the buggy file. This can be parsed to extract the commit id: `68a6301301378680519f2b146daec37812a1bc22`, the file name: `StringBuffer.java` and the original path within the project 
`core/src/classpath/java/java/lang/StringBuffer.java`

| Number of projects | Number of Instances |
| ------------------ |-------------------- |
| 12,577             | 2,735,506           |

## Dataset Creation

### Curation Rationale

Data is collected to train automated program repair (APR) models.

### Citation Information

```bib
@inproceedings{lutellierCoCoNuTCombiningContextaware2020,
  title = {{{CoCoNuT}}: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair},
  shorttitle = {{{CoCoNuT}}},
  booktitle = {Proceedings of the 29th {{ACM SIGSOFT International Symposium}} on {{Software Testing}} and {{Analysis}}},
  author = {Lutellier, Thibaud and Pham, Hung Viet and Pang, Lawrence and Li, Yitong and Wei, Moshi and Tan, Lin},
  year = {2020},
  month = jul,
  series = {{{ISSTA}} 2020},
  pages = {101--114},
  publisher = {{Association for Computing Machinery}},
  address = {{New York, NY, USA}},
  doi = {10.1145/3395363.3397369},
  url = {https://doi.org/10.1145/3395363.3397369},
  urldate = {2022-12-06},
  isbn = {978-1-4503-8008-9},
  keywords = {AI and Software Engineering,Automated program repair,Deep Learning,Neural Machine Translation}
}
```

code region:us
{"pretty_name": "CoCoNuT-C(2005)", "tags": ["code"]}
TAGS
#code #region-us 

Dataset Card for CoCoNuT-C(2005)
================================


Dataset Description
-------------------


* Homepage: CoCoNuT training data
* Repository: CoCoNuT repository
* Paper: CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair


### Dataset Summary


Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset.


### Languages


* C


Dataset Structure
-----------------


### Data Fields


The dataset consists of 4 columns: 'add', 'rem', 'context', and 'meta'.
These match the original dataset files: 'URL', 'URL', 'URL', and 'URL'.


### Data Instances


There is a mapping between the 4 columns for each instance.
For example:


5 first rows of 'rem' (i.e., the buggy line/hunk):


5 first rows of add (i.e., the fixed line/hunk):


These map to the 5 instances:


'context' contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of


is its associated function:


'meta' contains some metadata about the project:


'1056' is the project id. '/local/...' is the absolute path to the buggy file. This can be parsed to extract the commit id: '68a6301301378680519f2b146daec37812a1bc22', the file name: 'URL' and the original path within the project
'core/src/classpath/java/java/lang/URL'



Dataset Creation
----------------


### Curation Rationale


Data is collected to train automated program repair (APR) models.



### Dataset Summary


Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset. ### Languages


* C


Dataset Structure
----------------- ### Data Fields


The dataset consists of 4 columns: 'add', 'rem', 'context', and 'meta'.
These match the original dataset files: 'URL', 'URL', 'URL', and 'URL'. ### Data Instances


There is a mapping between the 4 columns for each instance.
For example:


5 first rows of 'rem' (i.e., the buggy line/hunk):


5 first rows of add (i.e., the fixed line/hunk):


These map to the 5 instances:


'context' contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of


is its associated function:


'meta' contains some metadata about the project:


'1056' is the project id. '/local/...' is the absolute path to the buggy file. This can be parsed to extract the commit id: '68a6301301378680519f2b146daec37812a1bc22', the file name: 'URL' and the original path within the project
'core/src/classpath/java/java/lang/URL'



Dataset Creation
---------------- ### Curation Rationale


Data is collected to train automated program repair (APR) models.
TAGS
#code #region-us 
 ### Dataset Summary


Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset. ### Languages


* C


Dataset Structure
----------------- ### Data Fields


The dataset consists of 4 columns: 'add', 'rem', 'context', and 'meta'.
These match the original dataset files: 'URL', 'URL', 'URL', and 'URL'. ### Data Instances


There is a mapping between the 4 columns for each instance.
For example:


5 first rows of 'rem' (i.e., the buggy line/hunk):


5 first rows of add (i.e., the fixed line/hunk):


These map to the 5 instances:


'context' contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of


is its associated function:


'meta' contains some metadata about the project:


'1056' is the project id. '/local/...' is the absolute path to the buggy file. This can be parsed to extract the commit id: '68a6301301378680519f2b146daec37812a1bc22', the file name: 'URL' and the original path within the project
'core/src/classpath/java/java/lang/URL'



Dataset Creation
---------------- ### Curation Rationale


Data is collected to train automated program repair (APR) models.
passage: TAGS
#code #region-us 
### Dataset Summary


Part of the data used to train the models in the "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" paper.
These datasets contain raw data extracted from GitHub, GitLab, and Bitbucket, and have neither been shuffled nor tokenized.
The year in the dataset’s name is the cutting year that shows the year of the newest commit in the dataset.### Languages


* C


Dataset Structure
-----------------### Data Fields


The dataset consists of 4 columns: 'add', 'rem', 'context', and 'meta'.
These match the original dataset files: 'URL', 'URL', 'URL', and 'URL'.### Data Instances


There is a mapping between the 4 columns for each instance.
For example:


5 first rows of 'rem' (i.e., the buggy line/hunk):


5 first rows of add (i.e., the fixed line/hunk):


These map to the 5 instances:


'context' contains the associated "context". Context is the (in-lined) buggy function (including the buggy lines and comments).
For example, the context of


is its associated function:


'meta' contains some metadata about the project:


'1056' is the project id. '/local/...' is the absolute path to the buggy file. This can be parsed to extract the commit id: '68a6301301378680519f2b146daec37812a1bc22', the file name: 'URL' and the original path within the project
'core/src/classpath/java/java/lang/URL'



Dataset Creation
----------------### Curation Rationale


Data is collected to train automated program repair (APR) models.