


### Dataset is imported from CodeXGLUE and pre-processed using their script.

# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/small in Semeru

## Task Definition

Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code. 
Models are evaluated by BLEU scores, accuracy (exactly match) and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD).

## Dataset

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. This dataset is small.



### Data Statistics

Data statistics of this dataset are shown in the below table:

|         | #Examples |
| ------- | :-------: | 
|         |   Small   |  
|  Train  |   46,680  |  
|  Valid  |    5,835  |   
|   Test  |    5,835  |   

# Reference
<pre><code>@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}
}</code></pre>
license:mit arxiv:1812.08693 region:us
{"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "May 2019 paper release date for https://arxiv.org/pdf/1812.08693.pdf", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"}
TAGS
#license-mit #arxiv-1812.08693 #region-us 

### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/small in Semeru


Task Definition
---------------


Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.


Dataset
-------


We use the dataset released by this paper(URL 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. This dataset is small.


### Data Statistics


Data statistics of this dataset are shown in the below table:



Reference
=========



```
@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}
}
```

### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/small in Semeru


Task Definition
---------------


Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.


Dataset
-------


We use the dataset released by this paper(URL 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. This dataset is small. ### Data Statistics


Data statistics of this dataset are shown in the below table:



Reference
=========



```
@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}
}
```
TAGS
#license-mit #arxiv-1812.08693 #region-us 
 ### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/small in Semeru


Task Definition
---------------


Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.


Dataset
-------


We use the dataset released by this paper(URL 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. This dataset is small. ### Data Statistics


Data statistics of this dataset are shown in the below table:



Reference
=========



```
@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}
}
```
passage: TAGS
#license-mit #arxiv-1812.08693 #region-us 
### Dataset is imported from CodeXGLUE and pre-processed using their script.


Where to find in Semeru:
========================


The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/small in Semeru


Task Definition
---------------


Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.


Dataset
-------


We use the dataset released by this paper(URL 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. This dataset is small.### Data Statistics


Data statistics of this dataset are shown in the below table:



Reference
=========



```
@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}
}
```