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
[1]FAN T T,XU L,CHEN L.Recommending Similar Bug Reports Based on Multi-Targets Optimization Algorithm NSGA-II[J].Chinese Journal of Computers,2019,42(10):2175-2189.
[2]ANVIK J,HIEW L,MURPHY G C.Coping with an open bug repository[C]//Proceedings of the 2005 OOPSLA.San Diego:ACM,2005:35-39.
[3]MINELLI R,MOCCI A,LANZA M.I know what you did last summer:an investigation of how developers spend their time[C]//Proceedings of ICPC 2015.Florence/Firenze:IEEE,2015:5-35.
[4]ROCHA H,VALENTE M T,MARQUES-NETO H,et al.An empirical study on recommendations of similar bugs[C]//Proceedings of SANER 2016.Osaka:IEEE,2016:46-56.
[5]RUNESON P,ALEXANDERSSON M,NYHOLM O.Detection of duplicate defect reports using natural language processing[C]//Proceedings of ICSE 2007.Minneapolis:IEEE,2007:499-510.
[6]WANG X,ZHANG L,XIE T,et al.An approach to detecting duplicate bug reports using natural language and execution information[C]//Proceedings of ICSE 2008.Leipzig:IEEE,2008:461-470.
[7]SUN C,LO D,WANG X,et al.A discriminative model approach for accurate duplicate bug report retrieval[C]//Proceedings of ICSE 2010.Cape Town:ACM,2010:45-54.
[8]SUN C,LO D,JIANG J,et al.Towards more accurate retrieval of duplicate bug reports[C]//Proceedings of ASE 2011.Lawrence:IEEE,2011:253-262.
[9]TIAN Y,SUN C,LO D.Improved duplicate bug report identification[C]//Proceedings of CSMR 2012.Szeged:IEEE,2012:385-390.
[10]YANG X L,LO D,XIA X,et al.Combining Word Embeddingwith Information Retrieval to Recommend Similar Bug Reports[C]//Proceedings of ISSRE 2016.Ottawa:IEEE,2016:127-137.
[11]BETTENBURG N,PREMRAJ R,ZIMMERMANN T.Dupli-cate bug reports considered harmful.Really?[C]//Proceedings of ICSM 2008.Beijing:IEEE,2008:337-345.
[12]HU D Y,CHEN M,WANG T,et al.Recommending Similar Bug Reports:A Novel Approach Using Document Embedding Model[C]//Proceedings of APSEC 2018.Nara:IEEE,2018:725-726.
[13]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[14]HENARD C,PAPADAKIS M,TRAON Y L.Mutation-basedgeneration of software product line test configurations[C]//Proceedings of SSBSE 2014.Bergamo:Springer,2014:92-106.
[15]DONG M H.Similar bug identification method based on bug report and source code[D].Harbin:Harbin Institute of Technology,2018.
[16]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[EB/OL].(2013-9-7)[2020-06-27].https://arxiv.org/abs/1301.3781.
[17]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of NIPS 2013.Lake Tahoe:NIPS Foundation,2013:3111-3119.
[18]ZIMMERMANN T,ZELLER A,WEISSGERBER P,et al.Minning version histories to guide software changes[J].IEEE Transactions,2005,31(6):429-445.
[19]CHEN C Y,GAO S,XING Z.Mining Analogical Libraries in Q&A Discussions-Incorporating Relational and Categorical Knowledge into Word Embedding[C]//Proceedings of SANER 2016.Suita:IEEE,2016:338-348.
[20]SHIHAB E,IHARA A,KAMEI Y,et al.Studying re-openedbugs in open source software[J].Empirical Software Enginee-ring,2013,18(5):1005-1042.
[21]YE X,BUNESCU R,LIU C.Learning to rank relevant files for bug reports using domain knowledge[C]//Proceedings of SIGSOFT FSE 2014.Hong Kong:ACM,2014:689-699.
[22]ZHOU J,ZHANG H,LO D.Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports[C]//Proceedings of ICSE 2012.Zurich:IEEE,2012:14-24.
[23]YE X,SHEN H,MA X,et al.From word embeddings to document similarities for improved information retrieval in software engineering[C]//Proceedings of ICSE 2016.Austin:IEEE,2016:404-415.
[24]KOCHHAR P S,XIA X,LO D,et al.Practitioners' expectations on automated fault localization[C]//Proceedings of ISSTA 2016.Saarbrücken:ACM,2016:165-176.
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