research-article

FixerCache: unsupervised caching active developers for diverse bug triage

Authors:

Qing WangAuthors Info & Claims

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Article No.: 25, Pages 1 - 10

https://doi.org/10.1145/2652524.2652536

Published: 18 September 2014 Publication History

Abstract

Context: Bug triage aims to recommend appropriate developers for new bugs in order to reduce time and effort in bug resolution. Most previous approaches for bug triage are supervised. Before recommending developers, these approaches need to learn developers' bug-fix preferences via building and training models using text-information of developers' historical bug reports.

Goal: In this paper, we empirically address three limitations of supervised bug triage approaches and propose FixerCache, an unsupervised approach for bug triage by caching developers based on their activeness in components of products.

Method: In FixerCache, each component of a product has a dynamic developer cache which contains prioritized developers according to developers' activeness scores. Given a new bug report, FixerCache recommends fixers with high activeness in developer cache to participate in fixing the new bug.

Results: Results of experiments on four products from Eclipse and Mozilla show that FixerCache outperforms supervised bug triage approaches in both prediction accuracy and diversity. And it can achieve prediction accuracy up to 96.32% and diversity up to 91.67%, with top-10 recommendation list.

Conclusions: FixerCache recommends fixers for new bugs based on developers' activeness in components of products with high prediction accuracy and diversity. Moreover, since FixerCache does not need to learn developers' bug-fix preferences through complex and time consuming processes, it could reduce bug triage time from hours of supervised approaches to seconds.

References

[1]

J. Anvik, L. Hiew, and G. C. Murphy, "Who Should Fix This Bug?," Proc. 28th Intl. Conf. Software Engineering (ICSE '06), May 2006, pp. 361--370.

Digital Library

[2]

D. Čubranić and G. C. Murphy, "Automatic Bug Triage Using Text Categorization," Proc. 16th Intl. Conf. Software Engineering & Knowledge Engineering (SEKE '04), Jun. 2004, pp. 92--97.

[3]

J. Xuan, H. Jiang, Z. Ren, and W. Zou, "Developer Prioritization in Bug Repositories," Proc. 34st Intl. Conf. Software Engineering (ICSE '12), June.2012, pp. 25--35.

Digital Library

[4]

G. Jeong, S. Kim, and T. Zimmermann, "Improving Bug Triage with Tossing Graphs," Proc. 17th ACM SIGSOFT Symp. Foundations of Software Engineering (FSE'09), Aug. 2009, pp. 111--120.

Digital Library

[5]

P. Bhattacharya and I. Neamtiu, "Fine-Grained Incremental Learning and Multi-Feature Tossing Graphs to Improve Bug Triaging," Proc. 26th IEEE Intl. Conf. Software Maintenance (ICSM '10), Sept. 2010, pp. 1--10.

Digital Library

[6]

A. Tamrawi, T. T. Nguyen, J. M. Al-Kofahi, and T. N. Nguyen, "Fuzzy Set and Cache-Based Approach for Bug Triaging," Proc. 19th ACM SIGSOFT Symp. Foundations of Software Engineering (FSE '11), Sept. 2011, pp. 365--375.

Digital Library

[7]

I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Morgan Kaufmann, Burlington, MA, 2011.

Digital Library

[8]

R. Shokripour, J. Anvik, Z. M. Kasirun, and S. Zamani, "Why So Complicated? Simple Term Filtering and Weighting for Location-Based Bug Report Assignment Recommendation," Proc. 10th IEEE Working Con. on Mining Software Repositories (MSR' 13), May. 2013, pp.2--11.

Digital Library

[9]

H. Naguib, N. Narayan, B. Brugge, and D. Helal, "Bug Report Assignment Recommendation using Activity Profiles," Proc. 10th IEEE Working Con. on Mining Software Repositories(MSR'13), May. 2013, pp.22--30.

Digital Library

[10]

X. Xie, W. Zhang, Y. Yang, and Q. Wang, "Dretom: developer recommendation based on topic models for bug resoluton," Proc. 8th International Conferenece on Predictive Models in Software Engineering (PROMISE'12), Mar. 2012, pp. 19--28.

Digital Library

[11]

W. Wu, W. Zhang, Y. Yang, and Q. Wang, "Drex: Developer recommendation with k-nearest-neighbor search and expertise ranking," in 18th Asia Pacific Software Engineering Conference (APSEC'11), Dec. 2011, pp. 389--396.

Digital Library

[12]

W. Zhang, Y. Yang, and Q. Wang, "An empirical study on identifying core developers using network analysis," Proc. 2nd Intl. Workshop on Evidential Assessment of Software Technologies (EAST'12), Sep. 2012, pp. 43--48.

Digital Library

[13]

Z. Lin, F. Shu, Y. Yang, C. Hu, and Q. Wang. "An empirical study on bug assignment automation using Chinese bug data," Proc. 3th ACM/IEEE Intl. Symp. Empirical Software Engineering and Measurement (ESEM'09), Oct. 2009, pp 451--455.

Digital Library

[14]

E. Murphy, T. Zimmermann, C. Bird, and N. Nagappan, "The design of bug fixes," Proc. 35th Intl. Conf. Software Engineering (ICSE '13) May. 2013, pp.332--341.

Digital Library

[15]

A. Gunawardana and G. Shani, "A Survey of Accuracy Evaluation Metrics of Recommendation Tasks," Journal of Machine Learning Research, Vol. 10, Dec. 2009, pp.2935--2962.

Digital Library

[16]

D. Matter, A. Kuhn, and O. Nierstrasz, "Assigning Bug Reports using a Vocabulary-based Expertise Model of Developers," Proc. 6th IEEE Working Con. on Mining Software Repositories (MSR' 09), May. 2009, pp 131--140.

Digital Library

[17]

N. Bettenburg, S. Just, A. Schröter, C. Weiss, R. Permraj, and T. Zimmermann, "What Makes a Good Bug Report," Proc. 16th ACM SIGSOFT Symp. Foundations of Software Engineering (FSE'08), Nov. 2008, pp. 308--318.

Digital Library

[18]

K. Herzig, S. Just, and A. Zeller, "It's not a bug, it's a feature: how misclassification impacts bug prediction," Proc. 35th Intl. Conf. Software Engineering (ICSE'13), May. 2013, pp.392--401.

Digital Library

[19]

J. Xuan, H. Jiang, Z. Ren, J. Yan, and Z. Lou, "Automatic Bug Triage Using Semi-Supervised Text Classification," Proc. 22th. Intl. Conf. Software Engineering & Knowledge Engineering (SEKE' 10), Jul. 2010, pp.209--214.

[20]

N. Bettenburg, S. Just, A. Schröter, C. Weiss, R. Permraj, and T. Zimmermann, "Quality of bug reports in Eclipse," Proc. OOPSLA workshop on eclipse technology eXchange (eclipse'07), Oct. 2007, pp. 21--25.

Digital Library

[21]

J. Xie, M. Zhou, and A. Mockus, "Impact of Triage: A Study of Mozilla and Gnome," Proc. 7th ACM/IEEE Intl. Symp. Empirical Software Engineering and Measurement (ESEM'13), Oct. 2013, pp. 247--250.

[22]

"The gnome bugsquad," https://live.gnome.org/Bugsquad, 2012.

[23]

"Mozilla triage guide -- harnessing the flood of community," https://wiki.mozilla.org/QA/Triage, 2010.

[24]

J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, USA

[25]

J. R. Sean M. McNee and J. A. Konstan, "Accurate is not always good: How accuracy metrics have hurt recommender systems," In extended abstracts on Human factors in computing systems (CHI'06), 2006, pp 1097--1101.

Digital Library

[26]

M. Zhang and N. Hurley, "Avoiding monotony: Improving the diversity of recommendation lists," Proc. 2nd ACM International Conf. Recommender Systems (RecSys'08), Oct.2008, pp. 123--130.

Digital Library

[27]

C. Yu, L. Lakshmanan, and S. A. Yahia, "It takes variety to make a world: diversification in recommender systems," Proc. 12th Intl. Conf. Extending Dtabase Technology: Advances in Database Technology (EDBT'09), Mar. 2009, pp.368--378.

Digital Library

[28]

S. Wang, W. Zhang, Y. Yang, and Q. Wang, "DevNet: Exploring Developer Collaboration in Heterogeneous Network of Bug Repositories," Proc. 7th ACM/IEEE Intl. Symp. Empirical Software Engineering and Measurement (ESEM'13), Oct. 2013, pp 193--202.

[29]

W. Zhang, S. Wang, Y. Yang, and Q. Wang "Heterogeneous Network Analysis of Developer Contribution in Bug Repositories," International Conference on Cloud and Service Computing (CSC'13), Nov. 2013, pp 98--105.

Digital Library

[30]

X. Xia, D. Lo, X. Wang, and B. Zhou "Accurate developer recommendation for bug resolution," 20th Working Conf. Reverse Engineering (WCRE'13), Oct. 2013, pp.72--81.

[31]

Q. Hong, S. Kim, S. C. Cheung, and C. Bird, "Understanding a Developer Social Network and its Evolution," Proc. 27th IEEE Intl. Conf. Software Maintenance (ICSM '11), Sept. 2011, pp. 323--332.

Digital Library

[32]

Z. Wen and C. Y. Lin, "On the Quality of Inferring Interests From Social Neighbors," Proc. 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'10), Jul. 2010, pp. 373--382.

Digital Library

[33]

S. Lin, X. Kong, and P. S. Yu, "Predicting trends in social networks via dynamic activeness model," Proc. 22nd ACM Intl. Conf. on Information & Knowledge Management (CIKM'13), Oct. 2013, pp. 1661--1666.

Digital Library

[34]

M. Ge, C. D. Battenfeld, and D. Jannach, "Beyond accuracy: evaluating recommender systems by coverage and serendipity" Proc. 4nd ACM Intl. Conf. Recommender Systems (RecSys'10), Sep.2010, pp. 257--260.

Digital Library

[35]

J. Xu, Y. Gao, S. Christley, and G. Madey, "A Topological Analysis of the Open Source Software Development Community," Proc. 38th Annual Hawaii Intl. Conf. on System Sciences (HICSS'05), Jan. 2005, Vol. 7.

Digital Library

Cited By

Xiao LWu YChen RShi H(2024)Optimizing Prioritization of Crowdsourced Test Reports of Web Applications through Image-to-Text ConversionSymmetry10.3390/sym1601008016:1(80)Online publication date: 8-Jan-2024
https://doi.org/10.3390/sym16010080
Laiq MAli NBörstler JEngström E(2024)Industrial adoption of machine learning techniques for early identification of invalid bug reportsEmpirical Software Engineering10.1007/s10664-024-10502-329:5Online publication date: 31-Jul-2024
https://doi.org/10.1007/s10664-024-10502-3
Dai JLi QXue HLuo ZWang YZhan S(2023)Graph collaborative filtering-based bug triagingJournal of Systems and Software10.1016/j.jss.2023.111667200(111667)Online publication date: Jun-2023
https://doi.org/10.1016/j.jss.2023.111667
Show More Cited By

Index Terms

FixerCache: unsupervised caching active developers for diverse bug triage
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
  2. Software notations and tools
    1. Software configuration management and version control systems

Recommendations

Improving bug triage with bug tossing graphs
ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering

bug report is typically assigned to a single developer who is then responsible for fixing the bug. In Mozilla and Eclipse, between 37%-44% of bug reports are "tossed" (reassigned) to other developers, for example because the bug has been assigned by ...
A hybrid bug triage algorithm for developer recommendation
SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports ...
Effective Bug Triage Based on Historical Bug-Fix Information
ISSRE '14: Proceedings of the 2014 IEEE 25th International Symposium on Software Reliability Engineering

For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

September 2014

461 pages

ISBN:9781450327749

DOI:10.1145/2652524

General Chair:
Maurizio Morisio
Politecnico di Torino, Italy
,
Program Chairs:
Tore Dybå
Sintef, Norway
,
Marco Torchiano
Politecnico di Torino, Italy

Copyright © 2014 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 the author(s) 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Natural Science Foundation of Beijing Municipality
State 863 High Technology R & D Project
National Science and Technology Major Project

Conference

ESEM '14

Sponsor:

SIGSOFT

ESEM '14: 2014 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 18 - 19, 2014

Torino, Italy

Acceptance Rates

ESEM '14 Paper Acceptance Rate 23 of 123 submissions, 19%;

Overall Acceptance Rate 130 of 594 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
355
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xiao LWu YChen RShi H(2024)Optimizing Prioritization of Crowdsourced Test Reports of Web Applications through Image-to-Text ConversionSymmetry10.3390/sym1601008016:1(80)Online publication date: 8-Jan-2024
https://doi.org/10.3390/sym16010080
Laiq MAli NBörstler JEngström E(2024)Industrial adoption of machine learning techniques for early identification of invalid bug reportsEmpirical Software Engineering10.1007/s10664-024-10502-329:5Online publication date: 31-Jul-2024
https://doi.org/10.1007/s10664-024-10502-3
Dai JLi QXue HLuo ZWang YZhan S(2023)Graph collaborative filtering-based bug triagingJournal of Systems and Software10.1016/j.jss.2023.111667200(111667)Online publication date: Jun-2023
https://doi.org/10.1016/j.jss.2023.111667
Jang JYang G(2022)A Bug Triage Technique Using Developer-Based Feature Selection and CNN-LSTM AlgorithmApplied Sciences10.3390/app1218935812:18(9358)Online publication date: 18-Sep-2022
https://doi.org/10.3390/app12189358
Wang JYang YWang SHu JWang Q(2022)Context- and Fairness-Aware In-Process Crowdworker RecommendationACM Transactions on Software Engineering and Methodology10.1145/348757131:3(1-31)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3487571
Zaidi SWoo HLee C(2022)A Graph Convolution Network-Based Bug Triage System to Learn Heterogeneous Graph Representation of Bug ReportsIEEE Access10.1109/ACCESS.2022.315307510(20677-20689)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3153075
Gupta CInácio PFreire M(2022)Improving software maintenance with improved bug triagingJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.10.01134:10(8757-8764)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2021.10.011
Shetty MBansal CKumar SRao NNagappan N(2022)SoftNER: Mining knowledge graphs from cloud incidentsEmpirical Software Engineering10.1007/s10664-022-10159-w27:4Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10664-022-10159-w
Laiq MAli NBöstler JEngström E(2022)Early Identification of Invalid Bug Reports in Industrial Settings – A Case StudyProduct-Focused Software Process Improvement10.1007/978-3-031-21388-5_34(497-507)Online publication date: 14-Nov-2022
https://doi.org/10.1007/978-3-031-21388-5_34
Wang JWang SChen JMenzies TCui QXie MWang Q(2021)Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced TestingIEEE Transactions on Software Engineering10.1109/TSE.2019.291852047:6(1259-1276)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TSE.2019.2918520
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