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Code Clone Detection Method Based on the Combination of Tree-Based and Token-Based Methods
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作者 Ryota Ami Hirohide Haga 《Journal of Software Engineering and Applications》 2017年第13期891-906,共16页
This article proposes the high-speed and high-accuracy code clone detection method based on the combination of tree-based and token-based methods. Existence of duplicated program codes, called code clone, is one of th... This article proposes the high-speed and high-accuracy code clone detection method based on the combination of tree-based and token-based methods. Existence of duplicated program codes, called code clone, is one of the main factors that reduces the quality and maintainability of software. If one code fragment contains faults (bugs) and they are copied and modified to other locations, it is necessary to correct all of them. But it is not easy to find all code clones in large and complex software. Much research efforts have been done for code clone detection. There are mainly two methods for code clone detection. One is token-based and the other is tree-based method. Token-based method is fast and requires less resources. However it cannot detect all kinds of code clones. Tree-based method can detect all kinds of code clones, but it is slow and requires much computing resources. In this paper combination of these two methods was proposed to improve the efficiency and accuracy of detecting code clones. Firstly some candidates of code clones will be extracted by token-based method that is fast and lightweight. Then selected candidates will be checked more precisely by using tree-based method that can find all kinds of code clones. The prototype system was developed. This system accepts source code and tokenizes it in the first step. Then token-based method is applied to this token sequence to find candidates of code clones. After extracting several candidates, selected source codes will be converted into abstract syntax tree (AST) for applying tree-based method. Some sample source codes were used to evaluate the proposed method. This evaluation proved the improvement of efficiency and precision of code clones detecting. 展开更多
关键词 Code Clone token-based DETECTION Tree-Based DETECTION TREE EDIT Distance
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基于DeBERTaV3的Token表达与联邦学习的高校图书馆推荐系统
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作者 赵霞 鄢源 《建模与仿真》 2025年第9期95-104,共10页
为提升高校图书馆推荐系统的个性化水平并保护用户隐私,本文提出一种融合联邦学习与Token级Transformer模型的图书推荐方法。该方法以Amazon Kindle Store数据集为基础,模拟多个高校图书馆作为联邦客户端,每个客户端本地训练模型,无需... 为提升高校图书馆推荐系统的个性化水平并保护用户隐私,本文提出一种融合联邦学习与Token级Transformer模型的图书推荐方法。该方法以Amazon Kindle Store数据集为基础,模拟多个高校图书馆作为联邦客户端,每个客户端本地训练模型,无需上传原始用户数据,从而有效保护读者隐私。在模型设计方面,本文采用DeBERTaV3作为Token-based Transformer骨干网络,将图书的标题与评论文本编码为多层次语义Token,实现对用户阅读行为和兴趣的深层建模。通过局部训练与中心聚合的联邦机制,构建出具备全局泛化能力的推荐模型。实验在Top-N推荐准确率、隐私保护强度与跨客户端迁移效果等维度进行了系统评估,结果表明所提出方法在保持推荐性能的同时兼顾数据安全性,适用于多校园、多用户异构环境下的个性化推荐任务。 展开更多
关键词 联邦学习 token-based TRANSFORMER DeBERTaV3 Top-N推荐
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