TGMM: Combining Parse Tree with GPU for Scalable Multilingual and Multi-Granularity Code Clone Detection
<|reference_start|>TGMM: Combining Parse Tree with GPU for Scalable Multilingual and Multi-Granularity Code Clone Detection: The rapid evolution of programming languages and software systems has necessitated the implementation of multilingual and scalable clone detection tools. However, it is difficult to achieve the above requirements at the same time. Most existing tools only focus on one challenge. In this work, we propose TGMM, a tree and GPU-based tool for multilingual and multi-granularity code clone detection. By generating parse trees based on user-provided grammar files, TGMM can extract code blocks at a specified granularity and detect Type-3 clones efficiently. In order to show the performance of TGMM, we compare it with seven state-of-the-art tools in terms of recall, precision, and execution time. TGMM ranks first in execution time and precision, while its recall is comparable to the others. Moreover, we analyzed the language extensibility of TGMM across 30 mainstream programming languages. Out of these, a total of 25 languages were supported, while the remaining five currently lack the necessary grammar files. Finally, we analyzed the clone characteristics of nine popular languages at five common granularities, hoping to inspire future researchers. The source code of TGMM is available at: https://github.com/TGMM24/TGMM.git.<|reference_end|>
@article{ye2024tgmm:,
    title={TGMM: Combining Parse Tree with GPU for Scalable Multilingual and
  Multi-Granularity Code Clone Detection},
    author={Yuhang Ye, Yuekun Wang, Yinxing Xue, Yueming Wu, Yang Liu},
    journal={arXiv preprint arXiv:2403.18202},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18202},
    primaryClass={cs.SE}
}