README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - code
license:
  - c-uda
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
pretty_name: CodeXGlueCcCloneDetectionPoj104
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: code
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 20179075
      num_examples: 32500
    - name: validation
      num_bytes: 6382433
      num_examples: 8500
    - name: test
      num_bytes: 7227506
      num_examples: 12000
  download_size: 13348734
  dataset_size: 33789014
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for "code_x_glue_cc_clone_detection_poj_104"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104

Dataset Summary

CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104

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 score. We use POJ-104 dataset on this task.

Supported Tasks and Leaderboards

• document-retrieval: The dataset can be used to train a model for retrieving top-k codes with the same semantics.

Languages

• C++ programming language

Dataset Structure

Data Instances

An example of 'train' looks as follows.

{
    "code": "\nint f(int shu,int min)\n{ \n  int k=1;\n  if(shu < min)\n  { \n    k= 0; \n   return k;\n  } \n  else\n {\n  for(int i = min;i<shu;i++)\n  { \n    if(shu%i == 0)\n    { \n         k=k+ f(shu/i,i); \n    } \n  \n    \n  } \n    return k; \n}\n} \n\nmain()\n{\n      int n,i,a;\n      scanf(\"%d\",&n);\n      \n      for(i=0;i<n;i++)\n      {\n          scanf(\"%d\",&a);\n          \n          if(i!=n-1)                                                        \n           printf(\"%d\\n\",f(a,2));\n           else\n           printf(\"%d\",f(a,2));                           \n                                      \n                     \n                      \n      }              \n                     \n                      \n                      }", 
    "id": 0, 
    "label": "home"
}

Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

default

field name	type	description
id	int32	Index of the sample
code	string	The full text of the function
label	string	The id of problem that the source code solves

Data Splits

name	train	validation	test
default	32000	8000	12000

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

https://github.com/microsoft, https://github.com/madlag

Licensing Information

Computational Use of Data Agreement (C-UDA) License.

Citation Information

@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}
}

Contributions

Thanks to @madlag (and partly also @ncoop57) for adding this dataset.