| | --- |
| | 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. |
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| |
|
| | ### 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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