Research on code entity recognition matching technology

Yijiang Xu, Yulong Wang, Fan Fang, Yonghong Rao
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引用次数: 0

Abstract

Nowadays, in the process of program development, it is unavoidable to use third-party code repositories, but the safety of third-party code is not guaranteed. Therefore, the research of software composition analysis (SCA) is very important. In this paper, we first proposed the value dependency analysis technology for feature extraction and entity recognition of code. From the level of functions, classes, files, packages, and projects, the normalized extraction of the intermediate representation of open-source code. In view of the insufficient accuracy of the existing models based on text, identification, syntax, and other analysis technologies, the value dependency analysis technology is used for the first time to build a more accurate normalized model of control flow and data flow, effectively improving the accuracy of feature extraction and anti-aliasing.
代码实体识别匹配技术研究
如今,在程序开发过程中,不可避免地要使用第三方代码库,但第三方代码的安全性却得不到保证。因此,软件组成分析(SCA)的研究具有十分重要的意义。本文首先提出了用于代码特征提取和实体识别的值依赖分析技术。从函数、类、文件、包和项目的层次,规范化抽取开源代码的中间表示形式。针对现有基于文本、识别、语法等分析技术的模型精度不足的问题,首次采用值依赖分析技术,构建了更为精确的控制流和数据流归一化模型,有效提高了特征提取和抗混叠的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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