| --- |
| license: mit |
| Programminglanguage: "C" |
| version: "N/A" |
| Date: "2015 POJ dataset from paper: https://arxiv.org/pdf/1409.5718.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/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 |
|
|
| 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 |
| - **label:** the number of problem that the source code solves |
| - **index:** the index of example |
|
|
| ### Data Statistics |
|
|
| Data statistics of the dataset are shown in the below table: |
|
|
| | | #Problems | #Examples | |
| | ----- | --------- | :-------: | |
| | Train | 64 | 32,000 | |
| | Dev | 16 | 8,000 | |
| | Test | 24 | 12,000 | |
|
|
| ## Reference |
| <pre><code>@inproceedings{mou2016convolutional, |
| title={Convolutional neural networks over tree structures for programming language processing}, |
| author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi}, |
| booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence}, |
| pages={1287--1293}, |
| year={2016} |
| }</code></pre> |
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|
