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--- |
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license: mit |
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Programminglanguage: "Java" |
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version: "N/A" |
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Date: "May 2019 paper release date for https://arxiv.org/pdf/1812.08693.pdf" |
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Contaminated: "Very Likely" |
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Size: "Standard Tokenizer " |
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--- |
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### Dataset is imported from CodeXGLUE and pre-processed using their script. |
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# Where to find in Semeru: |
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The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/medium in Semeru |
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## Task Definition |
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Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers. |
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In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code. |
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Models are evaluated by BLEU scores, accuracy (exactly match) and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD). |
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## Dataset |
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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. |
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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 |
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Data statistics of this dataset are shown in the below table: |
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| | #Examples | |
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| ------- | :-------: | |
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| | Medium | |
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| Train | 52,364 | |
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| Valid | 6,545 | |
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| Test | 6,545 | |
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# Reference |
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<pre><code>@article{tufano2019empirical, |
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title={An empirical study on learning bug-fixing patches in the wild via neural machine translation}, |
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author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys}, |
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journal={ACM Transactions on Software Engineering and Methodology (TOSEM)}, |
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volume={28}, |
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number={4}, |
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pages={1--29}, |
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year={2019}, |
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publisher={ACM New York, NY, USA} |
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}</code></pre> |
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