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.

References

[1]

A. Arcuri and G. Fraser. Parameter tuning or default values? an empirical investigation in search-based

[2]

B. Ashok, J. Joy, H. Liang, S. K. Rajamani, G. Srinivasa, and V. Vangala. Debugadvisor: a recommender system for debugging. In 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, pages 373–382. ACM, 2009.

Digital Library

[3]

N. Bettenburg, S. Just, A. Schröter, C. Weiss, R. Premraj, and T. Zimmermann. What makes a good bug report? In 16th ACM SIGSOFT International Symposium on Foundations of software engineering, pages 308–318. ACM, 2008.

Digital Library

[4]

N. Bettenburg, R. Premraj, T. Zimmermann, and S. Kim. Duplicate bug reports considered harmfulˆ a ˘ Ae really? In International Conference on Software maintenance (ICSM), pages 337–345, 2008.

[5]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003.

Digital Library

[6]

B. Bruegge and A. H. Dutoit. Object-Oriented Software Engineering Using UML, Patterns and Java-(Required). Prentice Hall, 2004.

Digital Library

[7]

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002.

Digital Library

[8]

D. Dreyton, A. A. Ara´ ujo, A. Dantas, Á. Freitas, and J. Souza. Search-based bug report prioritization for kate editor bugs repository. In International Symposium on Search Based Software Engineering, pages 295–300. Springer, 2015.

[9]

S. T. Dumais. Latent semantic analysis. Annual review of information science and technology, 38(1):188–230, 2004.

[10]

E. Enslen, E. Hill, L. Pollock, and K. Vijay-Shanker. Mining source code to automatically split identifiers for software analysis. In International Working Conference on Mining Software Repositories, pages 71–80. IEEE, 2009.

Digital Library

[11]

M. Fischer, M. Pinzger, and H. Gall. Analyzing and relating bug report data for feature tracking. In WCRE, volume 3, page 90, 2003.

Digital Library

[12]

D. E. Goldberg and K. Deb. A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms, 1:69–93, 1991.

[13]

M. Harman and B. F. Jones. Search-based software engineering. Information and software Technology, 43(14):833–839, 2001.

[14]

M. Harman, S. A. Mansouri, and Y. Zhang. Search-based software engineering: Trends, techniques and applications. ACM Computing Surveys (CSUR), 45(1):11, 2012.

Digital Library

[15]

C. Henard, M. Papadakis, and Y. Le Traon. Mutation-based generation of software product line test configurations. In International Symposium on Search Based Software Engineering, pages 92–106. Springer, 2014.

[16]

software engineering. Empirical Software Engineering, 18(3):594–623, 2013.

[17]

A. T. Nguyen, T. T. Nguyen, J. Al-Kofahi, H. V. Nguyen, and T. N. Nguyen. A topic-based approach for narrowing the search space of buggy files from a bug report. In IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 263–272, 2011.

Digital Library

[18]

A. N´ u˜ nez, M. G. Merayo, R. M. Hierons, and M. N´ u˜ nez. Using genetic algorithms to generate test sequences for complex timed systems. Soft Computing, 17(2):301–315, 2013.

Digital Library

[19]

S. Rao and A. Kak. Retrieval from software libraries for bug localization: a comparative study of generic and composite text models. In 8th Working Conference on Mining Software Repositories, pages 43–52, 2011.

Digital Library

[20]

R. K. Saha, J. Lawall, S. Khurshid, and D. E. Perry. On the effectiveness of information retrieval based bug localization for c programs. In ICSME, pages 161–170, 2014.

Digital Library

[21]

R. K. Saha, M. Lease, S. Khurshid, and D. E. Perry. Improving bug localization using structured information retrieval. In IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 345–355, 2013.

Digital Library

[22]

G. Salton, A. Wong, and C.-S. Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):613–620, 1975.

Digital Library

[23]

J. Shelburg, M. Kessentini, and D. R. Tauritz. Regression testing for model transformations: A multi-objective approach. In International Symposium on Search Based Software Engineering, pages 209–223. Springer, 2013.

Digital Library

[24]

C. Sun, D. Lo, X. Wang, J. Jiang, and S.-C. Khoo. A discriminative model approach for accurate duplicate bug report retrieval. In 32nd ACM/IEEE International Conference on Software Engineering-Volume 1, pages 45–54. ACM, 2010.

Digital Library

[25]

P.-N. Tan et al. Introduction to data mining. Pearson Education India, 2006.

[26]

C.-P. Wong, Y. Xiong, H. Zhang, D. Hao, L. Zhang, and H. Mei. Boosting bug-report-oriented fault localization with segmentation and stack-trace analysis. In ICSME, pages 181–190. Citeseer, 2014.

Digital Library

[27]

X. Ye, R. Bunescu, and C. Liu. Learning to rank relevant files for bug reports using domain knowledge. In 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, pages 689–699. ACM, 2014.

Digital Library

[28]

X. Ye, R. Bunescu, and C. Liu. Mapping bug reports to relevant files: A ranking model, a fine-grained benchmark, and feature evaluation. IEEE Transactions on Software Engineering, 42(4):379–402, 2016.

Digital Library

[29]

J. Zhou, H. Zhang, and D. Lo. Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports. In 34th International Conference on Software Engineering (ICSE), pages 14–24. IEEE, 2012.

Digital Library

Cited By

Shen YGao XSun HGuo Y(2025)Understanding vulnerabilities in software supply chainsEmpirical Software Engineering10.1007/s10664-024-10581-230:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10581-2
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
Show More Cited By

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

Recommendations

Learning to rank relevant files for bug reports using domain knowledge
FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how ...
Inferring test models from user bug reports using multi-objective search
Abstract
Bug reports are used by software testers to identify abnormal software behaviour. In this paper, we propose a multi-objective evolutionary approach to automatically generate finite state machines (FSMs) based on bug reports written in natural ...
An Effective Approach for Routing the Bug Reports to the Right Fixers
Internetware '18: Proceedings of the 10th Asia-Pacific Symposium on Internetware

Routing the bug reports to potential fixers (i.e., bug triaging), is an integral step in software development and maintenance. However, manually inspecting and assigning bug reports is tedious and time-consuming, especially in those software projects ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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]

Sponsors

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ASE'16

Sponsor:

SIGAI
SIGSOFT
IEEE-CS

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

September 3 - 7, 2016

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 82 of 337 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
435
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)4

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shen YGao XSun HGuo Y(2025)Understanding vulnerabilities in software supply chainsEmpirical Software Engineering10.1007/s10664-024-10581-230:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10581-2
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
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
Kangwanwisit PChoetkiertikul MRagkhitwetsagul CSunetnanta TMaipradit RHata HMatsumoto K(2022)A component recommendation model for issues in software projects2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE54890.2022.9836311(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.1109/JCSSE54890.2022.9836311
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents