research-article

An approach to detecting duplicate bug reports using natural language and execution information

Authors:

Jiasu SunAuthors Info & Claims

ICSE '08: Proceedings of the 30th international conference on Software engineering

Pages 461 - 470

https://doi.org/10.1145/1368088.1368151

Published: 10 May 2008 Publication History

Abstract

An open source project typically maintains an open bug repository so that bug reports from all over the world can be gathered. When a new bug report is submitted to the repository, a person, called a triager, examines whether it is a duplicate of an existing bug report. If it is, the triager marks it as DUPLICATE and the bug report is removed from consideration for further work. In the literature, there are approaches exploiting only natural language information to detect duplicate bug reports. In this paper we present a new approach that further involves execution information. In our approach, when a new bug report arrives, its natural language information and execution information are compared with those of the existing bug reports. Then, a small number of existing bug reports are suggested to the triager as the most similar bug reports to the new bug report. Finally, the triager examines the suggested bug reports to determine whether the new bug report duplicates an existing bug report. We calibrated our approach on a subset of the Eclipse bug repository and evaluated our approach on a subset of the Firefox bug repository. The experimental results show that our approach can detect 67%-93% of duplicate bug reports in the Firefox bug repository, compared to 43%-72% using natural language information alone.

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Cited By

Liao JXiao LXie XZhou XLi Y(2024)Clustering and Prioritization of Web Crowdsourced Test Reports Based on Text ClassificationInternational Journal of Web Services Research10.4018/IJWSR.35799921:1(1-19)Online publication date: 26-Oct-2024
https://doi.org/10.4018/IJWSR.357999
Al-Bayati JAl-Shamma MTawfeeq F(2024)Enhancement of Recommendation Engine Technique for Bug System FixesJournal of Advances in Information Technology10.12720/jait.15.4.555-56415:4(555-564)Online publication date: 2024
https://doi.org/10.12720/jait.15.4.555-564
Ahmed FDatta SNayebi M(2024)Negative Results of Image Processing for Identifying Duplicate Questions on Stack OverflowProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686688(281-291)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686688
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Index Terms

An approach to detecting duplicate bug reports using natural language and execution information
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

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Published In

cover image ACM Conferences

ICSE '08: Proceedings of the 30th international conference on Software engineering

May 2008

558 pages

ISBN:9781605580791

DOI:10.1145/1368088

General Chair:
Wilhelm Schäfer
University of Paderborn
,
Program Chairs:
Matthew B. Dwyer
University of Nebraska
,
Volker Gruhn
University of Leipzig

Copyright © 2008 ACM.

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Published: 10 May 2008

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May 10 - 18, 2008

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ICSE '08 Paper Acceptance Rate 56 of 370 submissions, 15%;

Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Liao JXiao LXie XZhou XLi Y(2024)Clustering and Prioritization of Web Crowdsourced Test Reports Based on Text ClassificationInternational Journal of Web Services Research10.4018/IJWSR.35799921:1(1-19)Online publication date: 26-Oct-2024
https://doi.org/10.4018/IJWSR.357999
Al-Bayati JAl-Shamma MTawfeeq F(2024)Enhancement of Recommendation Engine Technique for Bug System FixesJournal of Advances in Information Technology10.12720/jait.15.4.555-56415:4(555-564)Online publication date: 2024
https://doi.org/10.12720/jait.15.4.555-564
Ahmed FDatta SNayebi M(2024)Negative Results of Image Processing for Identifying Duplicate Questions on Stack OverflowProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686688(281-291)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686688
Zhang ZTawsif FRyu KYu THalfond W(2024)Mobile Bug Report Reproduction via Global Search on the App UI ModelProceedings of the ACM on Software Engineering10.1145/36608241:FSE(2656-2676)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660824
Götharsson MStahre KGay Gde Oliveira Neto FSaadatmand MLonetti FBudnik CLi JGuerriero A(2024)Exploring the Role of Automation in Duplicate Bug Report Detection: An Industrial Case StudyProceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)10.1145/3644032.3644450(193-203)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3644032.3644450
Yan YCooper NChaparro OMoran KPoshyvanyk DRoychoudhury APaiva AAbreu RStorey M(2024)Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug ReportsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639163(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639163
Patil AHan KJadon A(2024)A Comparative Analysis of Text Embedding Models for Bug Report Semantic Similarity2024 11th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN60856.2024.10512000(262-267)Online publication date: 21-Mar-2024
https://doi.org/10.1109/SPIN60856.2024.10512000
Zhang JXiao LLi MMeng ZLi Y(2024)HYDBre: A Hybrid Retrieval Method for Detecting Duplicate Software Bug Reports2024 11th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA63982.2024.00040(242-251)Online publication date: 2-Nov-2024
https://doi.org/10.1109/DSA63982.2024.00040
Chen BZou WCai BMeng QLiu WLi PChen L(2024)An empirical study on the potential of word embedding techniques in bug report management tasksEmpirical Software Engineering10.1007/s10664-024-10510-329:5Online publication date: 25-Jul-2024
https://doi.org/10.1007/s10664-024-10510-3
Tang XTian HKong PEzzini SLiu KXia XKlein JBissyandé T(2024)App review driven collaborative bug findingEmpirical Software Engineering10.1007/s10664-024-10489-x29:5Online publication date: 26-Jul-2024
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