
### 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>

license:mit arxiv:1409.5718 region:us
{"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"}
TAGS
#license-mit #arxiv-1409.5718 #region-us 

### 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 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:



Reference
---------



```
@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}
}
```


### 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 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:



Reference
---------



```
@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}
}
```
TAGS
#license-mit #arxiv-1409.5718 #region-us 
 ### 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 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:



Reference
---------



```
@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}
}
```
passage: TAGS
#license-mit #arxiv-1409.5718 #region-us 
### 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 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:



Reference
---------



```
@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}
}
```