research-article

Recommending relevant classes for bug reports using multi-objective search

Authors:

Marouane Kessentini,

Ali OuniAuthors Info & Claims

ASE '16: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering

Pages 286 - 295

https://doi.org/10.1145/2970276.2970344

Published: 25 August 2016 Publication History

Abstract

Developers may follow a tedious process to find the cause of a bug based on code reviews and reproducing the abnormal behavior. In this paper, we propose an automated approach to finding and ranking potential classes with the respect to the probability of containing a bug based on a bug report description. Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a multi-objective optimization algorithm. The relevance of the recommended classes (solution) is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. We evaluated our system on 6 open source Java projects, using the version of the project before fixing the bug of many bug reports. The experimental results show that the search-based approach significantly outperforms three state-of-the-art methods in recommending relevant files for bug reports. In particular, our multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 87% of the bug reports.

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Cited By

Malhotra RSingh MKhanna M(2025)Validating multi-objective search algorithms to predict faulty classesInternational Journal of System Assurance Engineering and Management10.1007/s13198-025-02717-4Online publication date: 3-Feb-2025
https://doi.org/10.1007/s13198-025-02717-4
Alsaedi SGad-Elrab ANoaman AEassa F(2024)Two-Level Information-Retrieval-Based Model for Bug Localization Based on Bug ReportsElectronics10.3390/electronics1302032113:2(321)Online publication date: 11-Jan-2024
https://doi.org/10.3390/electronics13020321
Vacheret RPérez FZiadi THillah L(2024)Boosting fault localization of statements by combining topic modeling and OchiaiInformation and Software Technology10.1016/j.infsof.2024.107499173(107499)Online publication date: Sep-2024
https://doi.org/10.1016/j.infsof.2024.107499
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Index Terms

Recommending relevant classes for bug reports using multi-objective search
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering

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Published In

cover image ACM Conferences

ASE '16: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering

August 2016

899 pages

ISBN:9781450338455

DOI:10.1145/2970276

General Chair:
David Lo
Singapore Management University, Singapore
,
Program Chairs:
Sven Apel
University of Passau, Germany
,
Sarfraz Khurshid
University of Texas at Austin, USA

Copyright © 2016 ACM.

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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SIGAI: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2016

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SIGAI
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ASE'16: ACM/IEEE International Conference on Automated Software Engineering

September 3 - 7, 2016

Singapore, Singapore

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Malhotra RSingh MKhanna M(2025)Validating multi-objective search algorithms to predict faulty classesInternational Journal of System Assurance Engineering and Management10.1007/s13198-025-02717-4Online publication date: 3-Feb-2025
https://doi.org/10.1007/s13198-025-02717-4
Alsaedi SGad-Elrab ANoaman AEassa F(2024)Two-Level Information-Retrieval-Based Model for Bug Localization Based on Bug ReportsElectronics10.3390/electronics1302032113:2(321)Online publication date: 11-Jan-2024
https://doi.org/10.3390/electronics13020321
Vacheret RPérez FZiadi THillah L(2024)Boosting fault localization of statements by combining topic modeling and OchiaiInformation and Software Technology10.1016/j.infsof.2024.107499173(107499)Online publication date: Sep-2024
https://doi.org/10.1016/j.infsof.2024.107499
Song YXie XXu B(2024)When debugging encounters artificial intelligence: state of the art and open challengesScience China Information Sciences10.1007/s11432-022-3803-967:4Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11432-022-3803-9
Shen YGao XSun HGuo Y(2024)Understanding vulnerabilities in software supply chainsEmpirical Software Engineering10.1007/s10664-024-10581-230:1Online publication date: 6-Nov-2024
https://dl.acm.org/doi/10.1007/s10664-024-10581-2
Fang CYu SSu TZhang JTian YLiu Y(2023)Test Report Generation for Android App Testing Via Heterogeneous Data AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2023.323724749:5(3032-3051)Online publication date: 1-May-2023
https://doi.org/10.1109/TSE.2023.3237247
Ali WBo LSun XWu XMemon SSiraj SSuwaree Ashton A(2023)Automated software bug localization enabled by meta-heuristic-based convolutional neural network and improved deep neural networkExpert Systems with Applications10.1016/j.eswa.2023.120562(120562)Online publication date: Jun-2023
https://doi.org/10.1016/j.eswa.2023.120562
Sangaroonsilp PChoetkiertikul MDam HGhose A(2023)An empirical study of automated privacy requirements classification in issue reportsAutomated Software Engineering10.1007/s10515-023-00387-930:2Online publication date: 28-Jun-2023
https://doi.org/10.1007/s10515-023-00387-9
Xia XLo D(2023)Information Retrieval‐Based Techniques for Software Fault LocalizationHandbook of Software Fault Localization10.1002/9781119880929.ch8(365-391)Online publication date: 21-Apr-2023
https://doi.org/10.1002/9781119880929.ch8
Polpinij JKaenampornpan MLuaphol B(2022)A Comparative Study of Short Text Classification Methods for Bug Report Type Identification2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C)10.1109/RI2C56397.2022.9910299(27-33)Online publication date: 4-Aug-2022
https://doi.org/10.1109/RI2C56397.2022.9910299
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