semeru/Code-Code-CloneDetection-POJ104
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… See the full description on the dataset page: https://huggingface.co/datasets/semeru/Code-Code-CloneDetection-POJ104.
Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
The dataset can be found at /nfs/semeru/semerudatasets/codexglue/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
<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>