license: mit
Programminglanguage: Java
version: N/A
Date: >-
2014 Big clone bench paper
https://www.cs.usask.ca/faculty/croy/papers/2014/SvajlenkoICSME2014BigERA.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-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
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
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}
}