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---
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 "
---
### 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/medium 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 medium.
### Data Statistics
Data statistics of this dataset are shown in the below table:
| | #Examples |
| ------- | :-------: |
| | Medium |
| Train | 52,364 |
| Valid | 6,545 |
| Test | 6,545 |
# 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>
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