Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (5): 91-98.doi: 10.11896/jsjkx.200600159

• Computer Software • Previous Articles     Next Articles

Approach for Knowledge-driven Similar Bug Report Recommendation

YU Sheng, LI Bin, SUN Xiao-bing, BO Li-li, ZHOU Cheng   

  1. School of Information Engineering,Yangzhou University,Yangzhou,Jiangsu 225127,China
    Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service,Yangzhou,Jiangsu 225127,China
  • Received:2020-06-28 Revised:2020-08-01 Online:2021-05-15 Published:2021-05-09
  • About author:YU Sheng,born in 1997,postgraduate.His main research interests include intelligent analysis of software data.(2822863494@qq.com)
    LI Bin,born in 1965,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include software engineering and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China (61972335,61872312),Yangzhou city-Yangzhou University Science and Technology Cooperation Fund Project (YZU201803) and Six Talent Peaks Project in Jiangsu Province (RJFW-053).

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Abstract: Software bug is inevitable in the process of software development,and the submitted bug reports are important source of information for bug analysis and fixing.Developers usually refer to similar historical bug reports and fixing solutions to analyze and fix the new bug at hand.This paper proposes an approach for knowledge-driven similar bug report recommendation.Based on the combination of information retrieval and Word Embedding,it constructs a bug knowledge graph.Then,it uses TF-IDF and Word Embedding to calculate the text similarity between bug reports,and generates the similarity of primary and secondary attributes between the bug reports.Finally,the above similarities are merged into a comprehensive similarity,and similar bug reports are recommended based on the comprehensive similarity.Experimental results show that the proposed approach improves the performance by an average of 12.7% on the Firefox dataset compared to the baseline method.

Key words: Information retrieval, Knowledge graph, Recommendation systems, Similar bug report, Word embedding

CLC Number: 

  • TP311
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