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Volume 40 Issue 1
March  2025
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Zhang FL, Chen YC, Khoo SC et al. CHANN: A hierarchical neural network for clone consistency prediction. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(1): 178−195, Jan. 2025. DOI: 10.1007/s11390-023-2831-8
Citation: Zhang FL, Chen YC, Khoo SC et al. CHANN: A hierarchical neural network for clone consistency prediction. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(1): 178−195, Jan. 2025. DOI: 10.1007/s11390-023-2831-8
shu

CHANN: A Hierarchical Neural Network for Clone Consistency Prediction

  • 1.

    School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

  • 2.

    School of Computing, National University of Singapore, Singapore 119077, Singapore

Funds: This work was partially supported by the National Natural Science Foundation of China under Grant Nos. U20A6003 and 62237001, the Guangdong Science and Technology Plan Project under Grant No. 2021B1212100004, the Guangdong Natural Science Fund Project under Grant No. 2021A1515011243, and Guangdong Joint Fund of the National Natural Science Foundation of China under Grant Nos. U1801263 and U1701262.
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  • Author Bio:

    Fan-Long Zhang received his Ph.D. degree in computer science from Harbin Institute of Technology, Harbin, in 2017. He is an assistant professor at the School of Computer Science and Technology at the Guangdong University of Technology, Guangzhou. His research interests include program analysis, code clones, software engineering, and artificial intelligence

    Yu-Chen Chen received his B.S. degree in computer science from Guangdong University of Technology, Guangzhou, in 2022. He is currently a Ph.D. candidate in software engineering of Nanjing University, Nanjing. His research interests include software engineering and software security

    Siau-Cheng Khoo is an associate professor in the School of Computing and Co-Director of the NUS Business Analytics Centre at National University of Singapore (NUS), Singapore. His research focuses on improving the productivity of software developers, and he has expertise in both the formal and practical aspects of programming languages and software engineering

    Wen-Chao Jiang received his Ph.D. degree in computer science from Huazhong University of Science and Technology, Wuhan, in 2009. He is currently an associate professor with the School of Computer Science and Technology, Guangdong University of Technology, Guangzhou. His research interests include AI algorithms and their application, graph theory, complex networks, cloud computing, and distributed computing

  • Corresponding author:

    jiangwenchao@gdut.edu.cn

  • Received Date: September 13, 2022
  • Accepted Date: December 06, 2023
    Abstract

  • Modifying a code segment may give rise to a consistency issue when the code segment belongs to a clone group comprising closely similar code segments. Recent studies have demonstrated that such consistent changes can incur extra maintenance costs when clones are checked for consistency and introduce defects if developers forget to change clones consistently when needed. To address this problem, researchers have proposed an approach to predict clone consistency in advance with handcrafted attributes, notably using machine learning methods. Although these attributes can help predict clone consistency to some extent, the capability of such an approach is generally weak and unsatisfactory in practice. Such limitations in capability are especially severe at a project’s infancy stage when there is not sufficient within-project data to model clone consistency behavior, and cross-project data have not been helpful in supporting prediction. In this paper, we propose the Clone Hierarchical Attention Neural Network (CHANN) to represent code clones and their evolution by adopting a hierarchical perspective of code, context, and code evolution, and thus enhancing the effectiveness of clone consistency prediction. To assess the effectiveness of CHANN, we conduct experiments on the dataset collected from eight open-source projects. The experimental results show that CHANN is highly effective in predicting clone consistency, and the precision, recall, and F-measure attained in prediction are around 82%. These findings support our hypothesis that the hierarchical neural network can help developers predict clone consistency effectively in the case of cross-project incubation when insufficient data are available at the early stage of software development.

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