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TPCaps: a framework for code clone detection and localization based on improved CapsNet

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Abstract

In this paper, we propose TPCaps, a new code clone detection framework for addressing the inefficiency of semantic clone detection and the difficulty in locating code clones. Based on CapsNet with tokens and Program Dependence Graph (PDG), TPCaps can improve the processing rate and the capability for detecting. Firstly, TPCaps determines tokens by dataset partitioning and semantic signature, filtering out the valid code clone. Then, using the tokens mentioned above, it can detect Type-1 and Type-2 clones effectively. In addition, TPcaps generates PDG that is composed of data dependencies and control dependencies extracted from codes. Using PDG as input, the improved capsule network, we called RCapsNet, is able to detect Type-3 and Type-4 clones. Based on the CapsNet, RCapsNet introduces selective search algorithm combines with the Regional Proposal Network (RPN), where CapsNet handles the clone features to achieve detection and classification, and RPN processes the location information and updates trains the candidate frames to obtain a specific clone location. In the experimental section, we evaluate the recall and precision of the model. TPCaps shows its high accuracy compared to other models.

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Data availability statement

The processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Acknowledgements

This work was supported in part by the State Grid Jiangxi Information & Telecommunication Company Project under Grant 52183520007V.

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, NO.2 Beinong Road, Beijing, 102206, China

    Yuancheng Li, Chaohang Yu & Yaqi Cui

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  1. Yuancheng Li
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  2. Chaohang Yu
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  3. Yaqi Cui
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Contributions

Yuancheng Li contributed to the conception of the study;

Chaohang Yu designed the experiment and wrote the manuscript;

Yaqi Cui performed the experiment, performed the data analyses and wrote the manuscript.

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Correspondence to Yuancheng Li.

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Li, Y., Yu, C. & Cui, Y. TPCaps: a framework for code clone detection and localization based on improved CapsNet. Appl Intell 53, 16594–16605 (2023). https://doi.org/10.1007/s10489-022-03158-3

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