电子学报 ›› 2021, Vol. 49 ›› Issue (4): 661-664.DOI: 10.12263/DZXB.20200327

• 学术论文 • 上一篇    下一篇

面向缺陷知识的多特征匹配搜索算法

李斌1,2, 陈定山1, 孙小兵1,2,3, 薄莉莉1,2,3   

  1. 1. 扬州大学信息工程学院, 江苏扬州 225127;
    2. 江苏省知识管理与智能服务工程研究中心, 江苏扬州 225127;
    3. 南京大学计算机软件新技术国家重点实验室, 江苏南京 210023
  • 收稿日期:2020-04-01 修回日期:2020-10-06 出版日期:2021-04-25
    • 作者简介:
    • 李斌 男,1965年12月出生,江苏靖江人,扬州大学信息工程学院教授、博士,主要研究方向为智能软件工程.E-mail:lb@yzu.edu.cn;陈定山 男,1995年1月出生,江苏连云港人,扬州大学信息工程学院硕士,主要研究方向为缺陷分析.E-mail:MX120170402@yzu.edu.cn
    • 基金资助:
    • 国家自然科学基金 (No.61872312,No.61972335,No.62002309); 扬州市校合作项目 (No.YZU201803); 南京大学计算机软件新技术国家重点实验室资助项目 (No.KFKT2020B15,No.KFKT2020B16); 扬州大学"高端人才支持计划"

Multi-Feature Matching Search Algorithm for Bug Knowledge

LI Bin1,2, CHEN Ding-shan1, SUN Xiao-bing1,2,3, BO Li-li1,2,3   

  1. 1. School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China;
    2. Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou, Jiangsu 225127, China;
    3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, China
  • Received:2020-04-01 Revised:2020-10-06 Online:2021-04-25 Published:2021-04-25
    • Supported by:
    • National Natural Science Foundation of China (No.61872312, No.61972335, No.62002309); Cooperation Program of Yangzhou Universities and Government (No.YZU201803); State Key Laboratory for Novel Software Technology at Nanjing University (No.KFKT2020B15, No.KFKT2020B16); Yangzhou University High-end Talent Support Program

摘要: 缺陷数据分析正成为软件工程领域的热点,现有缺陷分析技术无法有效处理复杂和冗余的缺陷数据,以高效地辅助缺陷修复工作.本文提出一种多特征匹配搜索算法——MMSBK (Multi-feature Matching Search Algorithm for Bug Knowledge).首先对缺陷问题进行分析,抽取其包含的缺陷实体及关系;然后,基于实体和关系匹配将缺陷问题与缺陷知识图谱关联,通过知识图谱的关联性和可视化帮助软件开发搜索缺陷知识;最后,基于匹配算法生成的缺陷关系三元组生成搜索结果子图.实验验证了MMSBK算法的有效性.

关键词: 缺陷知识图谱, 知识搜索, 缺陷实体, 缺陷关系

Abstract: Bug data analysis is becoming a hotspot in the software engineering domain. The accumulation of bug knowledge requires redefinition of a new search method to effectively process complex and redundant bug data to efficiently assist bug fixing. This paper proposes a multi-feature matching search algorithm for bug knowledge (MMSBK). First, we analyze the bug question and extract the bug entities and relations. Then, based on bug entity and relation matching, the bug question is associated with the bug knowledge graph. Finally, the search sub-graph is generated based on the bug triples generated by the matching algorithm. The experiment shows the effectiveness of MMSBK.

Key words: bug knowledge graph, knowledge search, bug entity, bug relation

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