semeru/Code-Code-CloneDetection-BigCloneBench

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-BigCloneBench in Semeru

CodeXGLUE -- Clone Detection (BCB)

Task Definition

Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.

Updates

2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of "macro" F1 score.

Dataset

The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.

Data Format

1. dataset/data.jsonl is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.

   • func: the function

   • idx: index of the example

2. train.txt/valid.txt/test.txt provide examples, stored in the following format: idx1 idx2 label

Data Statistics

Data statistics of the dataset are shown in the below table:

	#Examples
Train	901,028
Dev	415,416
Test	415,416

Reference

@inproceedings{svajlenko2014towards,
  title={Towards a big data curated benchmark of inter-project code clones},
  author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
  booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
  pages={476--480},
  year={2014},
  organization={IEEE}
}

@inproceedings{wang2020detecting,
  title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
  author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
  booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={261--271},
  year={2020},
  organization={IEEE}
}