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---
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
# CodeXGLUE -- Clone Detection (POJ-104)
## Task Definition
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.
## Dataset
We use [POJ-104](https://arxiv.org/pdf/1409.5718.pdf) dataset on this task.
### Data Format
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **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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