| --- |
| 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/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. |
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|
|
|
|
|
| ### 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> |
