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Augmented bug localization using past bug information

Author:

Brent D. NicholsAuthors Info & Claims

ACMSE '10: Proceedings of the 48th annual ACM Southeast Conference

Article No.: 61, Pages 1 - 6

https://doi.org/10.1145/1900008.1900090

Published: 15 April 2010 Publication History

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Abstract

Traditional bug localization techniques involve a developer using his or her knowledge of the software system to locate bugs in source code. Various automated techniques simulate knowledge of the system using source code retrieval models such as latent semantic indexing (LSI) and latent Dirichlet allocation (LDA). While these methods do an adequate job, they do not make use of another wealth of information stored in the form of past bug reports. In this paper, I present an extension to the LSI model for bug localization in which the information stored in past bug reports augments the LSI model of bug localization. I describe the details of implementing this process along with the novel patch cartographer tool that is necessary for its execution. Presented along with this description is a pair of case studies verifying the effectiveness of the patch cartographer and process respectively. Results show that the patch cartographer indeed correctly identifies affected methods from a patch file. Additionally, the study of the augmented process shows significant improvement in performance compared to LSI alone.

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Cited By

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zhao YLi XTian QDeng WLi YZhang YSong J(2023)Impact analysis of bug localization accuracy oriented to bug reportSixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)10.1117/12.3004582(42)Online publication date: 16-Aug-2023
https://doi.org/10.1117/12.3004582
Liu CXia XLo DGao CYang XGrundy J(2021)Opportunities and Challenges in Code Search ToolsACM Computing Surveys10.1145/348002754:9(1-40)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3480027
Razzaq ABuckley JPatten JChochlov MSai A(2021)BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM52516.2021.00019(81-91)Online publication date: Sep-2021
https://doi.org/10.1109/SCAM52516.2021.00019
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Index Terms

Augmented bug localization using past bug information
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation
        Walkthroughs
      2. Software defect analysis
        Software testing and debugging

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Published In

ACMSE '10: Proceedings of the 48th annual ACM Southeast Conference

April 2010

488 pages

ISBN:9781450300643

DOI:10.1145/1900008

Conference Chair:
H. Conrad Cunningham
University of Mississippi
,
Program Chairs:
Paul Ruth,
Nicholas A. Kraft

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 April 2010

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ACM SE '10

Sponsor:

ACM SE '10: ACM Southeast Regional Conference

April 15 - 17, 2010

Mississippi, Oxford

Acceptance Rates

ACMSE '10 Paper Acceptance Rate 48 of 94 submissions, 51%;

Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all

zhao YLi XTian QDeng WLi YZhang YSong J(2023)Impact analysis of bug localization accuracy oriented to bug reportSixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)10.1117/12.3004582(42)Online publication date: 16-Aug-2023
https://doi.org/10.1117/12.3004582
Liu CXia XLo DGao CYang XGrundy J(2021)Opportunities and Challenges in Code Search ToolsACM Computing Surveys10.1145/348002754:9(1-40)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3480027
Razzaq ABuckley JPatten JChochlov MSai A(2021)BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM52516.2021.00019(81-91)Online publication date: Sep-2021
https://doi.org/10.1109/SCAM52516.2021.00019
Akbar SKak A(2020)A Large-Scale Comparative Evaluation of IR-Based Tools for Bug LocalizationProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387474(21-31)Online publication date: 29-Jun-2020
https://dl.acm.org/doi/10.1145/3379597.3387474
Chaparro OFlorez JMarcus A(2019)Using bug descriptions to reformulate queries during text-retrieval-based bug localizationEmpirical Software Engineering10.1007/s10664-018-9672-z24:5(2947-3007)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10664-018-9672-z
Dilshener TWermelinger MYu Y(2018)Locating bugs without looking backAutomated Software Engineering10.1007/s10515-017-0226-125:3(383-434)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10515-017-0226-1
Chaparro OFlorez JMarcus A(2017)Using Observed Behavior to Reformulate Queries during Text Retrieval-based Bug Localization2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2017.100(376-387)Online publication date: Sep-2017
https://doi.org/10.1109/ICSME.2017.100
Rahman SRahman MSakib K(2017)An Improved Method Level Bug Localization Approach Using Minimized Code SpaceEvaluation of Novel Approaches to Software Engineering10.1007/978-3-319-56390-9_9(179-200)Online publication date: 7-Apr-2017
https://doi.org/10.1007/978-3-319-56390-9_9
Rahman SGanguly KSakib K(2015)An improved bug localization using structured information retrieval and version history2015 18th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCITechn.2015.7488066(190-195)Online publication date: Dec-2015
https://doi.org/10.1109/ICCITechn.2015.7488066
Wu LDu LLiu BXu GGe YFu YLi JZhou YXiong H(2014)Heterogeneous Metric Learning with Content-Based Regularization for Software Artifact RetrievalProceedings of the 2014 IEEE International Conference on Data Mining10.1109/ICDM.2014.147(610-619)Online publication date: 14-Dec-2014
https://dl.acm.org/doi/10.1109/ICDM.2014.147
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Information retrieval and spectrum based bug localization: better together

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