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--- |
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license: mit |
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Programminglanguage: "C" |
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version: "N/A" |
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Date: "2015 POJ dataset from paper: https://arxiv.org/pdf/1409.5718.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/Clone-detection-POJ-104 in Semeru |
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# CodeXGLUE -- Clone Detection (POJ-104) |
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## Task Definition |
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Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset. |
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## Dataset |
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We use [POJ-104](https://arxiv.org/pdf/1409.5718.pdf) dataset on this task. |
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### Data Format |
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For each file, each line in the uncompressed file represents one function. One row is illustrated below. |
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- **code:** the source code |
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- **label:** the number of problem that the source code solves |
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- **index:** the index of example |
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### Data Statistics |
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Data statistics of the dataset are shown in the below table: |
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| | #Problems | #Examples | |
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| ----- | --------- | :-------: | |
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| Train | 64 | 32,000 | |
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| Dev | 16 | 8,000 | |
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| Test | 24 | 12,000 | |
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## Reference |
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<pre><code>@inproceedings{mou2016convolutional, |
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title={Convolutional neural networks over tree structures for programming language processing}, |
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author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi}, |
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booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence}, |
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pages={1287--1293}, |
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year={2016} |
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}</code></pre> |
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