poster

Improving bug localization with report quality dynamics and query reformulation

Authors:

Mohammad Masudur Rahman,

Chanchai K. RoyAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Pages 348 - 349

https://doi.org/10.1145/3183440.3195003

Published: 27 May 2018 Publication History

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Abstract

Recent findings from a user study suggest that IR-based bug localization techniques do not perform well if the bug report lacks rich structured information such as relevant program entity names. On the contrary excessive structured information such as stack traces in the bug report might always not be helpful for the automated bug localization. In this paper, we conduct a large empirical study using 5,500 bug reports from eight subject systems and replicating three existing studies from the literature. Our findings (1) empirically demonstrate how quality dynamics of bug reports affect the performances of IR-based bug localization, and (2) suggest potential ways (e.g., query reformulations) to overcome such limitations.

References

[1]

2018. Empirical Dataset. (2018). http://www.usask.ca/~mor543/icse2018

Google Scholar

[2]

A. Bachmann and A. Bernstein. 2009. Software Process Data Quality and Characteristics: A Historical View on Open and Closed Source Projects. In Proc. IWPSE. 119--128.

Digital Library

Google Scholar

[3]

L. Moreno, J. J. Treadway, A. Marcus, and W. Shen. 2014. On the Use of Stack Traces to Improve Text Retrieval-Based Bug Localization. In Proc. ICSME. 151--160.

Digital Library

Google Scholar

[4]

A. T. Nguyen, T. T. Nguyen, J. Al-Kofahi, H. V. Nguyen, and T. N. Nguyen. 2011. A Topic-based Approach for Narrowing the Search Space of Buggy Files from a Bug Report. In Proc. ASE. 263--272.

Digital Library

Google Scholar

[5]

S. Rao and A. Kak. 2011. Retrieval from Software Libraries for Bug Localization: A Comparative Study of Generic and Composite Text Models. In Proc. MSP. 43--52.

Digital Library

Google Scholar

[6]

R. K. Saha, M. Lease, S. Khurshid, and D. E. Perry. 2013. Improving bug localization using structured information retrieval. In Proc. ASE. 345--355.

Digital Library

Google Scholar

[7]

Q. Wang, C. Parnin, and A. Orso. 2015. Evaluating the Usefulness of IR-based Fault Localization Techniques. In Proc. ISSTA. 1--11.

Digital Library

Google Scholar

[8]

J. Zhou, H. Zhang, and D. Lo. 2012. Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports. In Proc. ICSE. 14--24.

Digital Library

Google Scholar

Cited By

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Li WLi QMing YDai WYing SYuan M(2022)An empirical study of the effectiveness of IR-based bug localization for large-scale industrial projectsEmpirical Software Engineering10.1007/s10664-021-10082-627:2Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10664-021-10082-6
Rahman MKhomh FYeasmin SRoy C(2021)The forgotten role of search queries in IR-based bug localization: an empirical studyEmpirical Software Engineering10.1007/s10664-021-10022-426:6Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s10664-021-10022-4
Zhu ZLi YWang YWang YTong H(2021)A deep multimodal model for bug localizationData Mining and Knowledge Discovery10.1007/s10618-021-00755-735:4(1369-1392)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10618-021-00755-7
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Index Terms

Improving bug localization with report quality dynamics and query reformulation
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools
    1. Software maintenance tools

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Information & Contributors

Information

Published In

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

May 2018

231 pages

ISBN:9781450356633

DOI:10.1145/3183440

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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Poster

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ICSE '18

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SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

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

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Cited By

View all

Li WLi QMing YDai WYing SYuan M(2022)An empirical study of the effectiveness of IR-based bug localization for large-scale industrial projectsEmpirical Software Engineering10.1007/s10664-021-10082-627:2Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10664-021-10082-6
Rahman MKhomh FYeasmin SRoy C(2021)The forgotten role of search queries in IR-based bug localization: an empirical studyEmpirical Software Engineering10.1007/s10664-021-10022-426:6Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s10664-021-10022-4
Zhu ZLi YWang YWang YTong H(2021)A deep multimodal model for bug localizationData Mining and Knowledge Discovery10.1007/s10618-021-00755-735:4(1369-1392)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10618-021-00755-7
Yuan WWang PGuo YHe LHe T(2020)Mining the Software Engineering Forums: What’s New and What’s LeftWeb Information Systems and Applications10.1007/978-3-030-60029-7_46(513-524)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_46
Kim MLee EHung CPapadopoulos G(2019)A novel approach to automatic query reformulation for IR-based bug localizationProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297451(1752-1759)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297451
Rahman MRoy CLeavens GGarcia APăsăreanu C(2018)Improving IR-based bug localization with context-aware query reformulationProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236065(621-632)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236065

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