research-article

Automated Classification of Software Bug Reports

Authors:

Ahmed Fawzi Otoom,

Sara Al-jdaeh,

Maen HammadAuthors Info & Claims

ICICM '19: Proceedings of the 9th International Conference on Information Communication and Management

Pages 17 - 21

https://doi.org/10.1145/3357419.3357424

Published: 23 August 2019 Publication History

Get Access

Abstract

We target the problem of software bug reports classification. Our main aim is to build a classifier that is capable of classifying newly incoming bug reports into two predefined classes: corrective (defect fixing) report and perfective (major maintenance) report. This helps maintainers to quickly understand these bug reports and hence, allocate resources for each category. For this purpose, we propose a distinctive feature set that is based on the occurrences of certain keywords. The proposed feature set is then fed into a number of classification algorithms for building a classification model. The results of the proposed feature set achieved high accuracy in classification with SVM classification algorithm reporting an average accuracy of (93.1%) on three different open source projects.

References

[1]

Bettenburg, N., Premraj, R., Zimmermann, T. and Kim, S., 2008, September. Duplicate bug reports considered harmful... really?. In 2008 IEEE International Conference on Software Maintenance (pp. 337--345). IEEE.

Google Scholar

[2]

Tian, Y., Lo, D. and Sun, C., 2012, October. Information retrieval based nearest neighbor classification for fine- grained bug severity prediction. In 2012 19th Working Conference on Reverse Engineering (pp. 215--224). IEEE.

Google Scholar

[3]

Anvik, J., Hiew, L. and Murphy, G.C., 2006, May. Who should fix this bug?. In Proceedings of the 28th international conference on Software engineering (pp. 361--370).

Google Scholar

[4]

Levin, S. and Yehudai, A., 2017, November. Boosting automatic commit classification into maintenance activities by utilizing source code changes. In Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering (pp. 97--106). ACM.

Google Scholar

[5]

Pandey, N., Sanyal, D.K., Hudait, A. and Sen, A., 2017. Automated classification of software issue reports using machine learning techniques: an empirical study. Innovations in Systems and Software Engineering, 13(4), pp. 279--297.

Digital Library

Google Scholar

[6]

Antoniol, G., Ayari, K., Di Penta, M., Khomh, F. and Guéhéneuc, Y.G., 2008, October. Is it a bug or an enhancement?: a text-based approach to classify change requests. In CASCON (Vol. 8, pp. 304--318).

Google Scholar

[7]

Zhou, Y., Tong, Y., Gu, R. and Gall, H., 2016. Combining text mining and data mining for bug report classification. Journal of Software: Evolution and Process, 28(3), pp. 150--176.

Digital Library

Google Scholar

[8]

Pingclasai, N., Hata, H. and Matsumoto, K.I., 2013, December. Classifying bug reports to bugs and other requests using topic modeling. In 2013 20th Asia-Pacific Software Engineering Conference (APSEC) (Vol. 2, pp. 13--18). IEEE

Google Scholar

[9]

Fu, Y., Yan, M., Zhang, X., Xu, L., Yang, D. and Kymer, J.D., 2015. Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation. Information and Software Technology, 57, pp. 369--377.

Crossref

Google Scholar

[10]

Limsettho, N., Hata, H., Monden, A. and Matsumoto, K., 2014, November. Automatic unsupervised bug report categorization. In 2014 6th International Workshop on Empirical Software Engineering in Practice (pp. 7--12). IEEE.

Google Scholar

[11]

Tian, K., Revelle, M. and Poshyvanyk, D., 2009, May. Using latent dirichlet allocation for automatic categorization of software. In 2009 6th IEEE International Working Conference on Mining Software Repositories (pp. 163--166). IEEE.

Google Scholar

[12]

Zibran, M.F., 2016, May. On the effectiveness of labeled latent dirichlet allocation in automatic bug-report categorization. In 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C) (pp. 713--715). IEEE.

Google Scholar

[13]

Hindle, A., German, D.M., Godfrey, M.W. and Holt, R.C., 2009, May. Automatic classication of large changes into maintenance categories. In 2009 IEEE 17th International Conference on Program Comprehension (pp. 30--39). IEEE.

Google Scholar

[14]

Witten, I.H. and Frank, E., Data Mining: Practical machine learning tools with Java implementations. 2000.

Digital Library

Google Scholar

Cited By

View all

Said MBin Faiz RAljaidi MAlshammari M(2024)Comparative analysis of impact of classification algorithms on security and performance bug reportsJournal of Intelligent Systems10.1515/jisys-2024-004533:1Online publication date: 4-Dec-2024
https://doi.org/10.1515/jisys-2024-0045
Du XLi CMa XZheng ZRoychoudhury APaiva AAbreu RStorey M(2024)How Does Pre-trained Language Model Perform on Deep Learning Framework Bug Prediction?Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3643113(346-347)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3643113
Xu YLi Y(2024)A New Method of Security Bug Reports AnalysisIT Professional10.1109/MITP.2023.329852026:2(49-56)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MITP.2023.3298520
Show More Cited By

Index Terms

Automated Classification of Software Bug Reports
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software

Recommendations

Classifying Bug Reports to Bugs and Other Requests Using Topic Modeling
APSEC '13: Proceedings of the 2013 20th Asia-Pacific Software Engineering Conference (APSEC) - Volume 02

Bug reports are widely used in several research areas such as bug prediction, bug triaging, and etc. The performance of these studies relies on the information from bug reports. Previous study showed that a significant number of bug reports are actually ...
Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment ...
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

Information & Contributors

Information

Published In

ICICM '19: Proceedings of the 9th International Conference on Information Communication and Management

August 2019

210 pages

ISBN:9781450371889

DOI:10.1145/3357419

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]

In-Cooperation

Chinese Academy of Sciences

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICICM 2019

ICICM 2019: The 9th International Conference on Information Communication and Management

August 23 - 26, 2019

Prague, Czech Republic

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
320
Total Downloads

Downloads (Last 12 months)42
Downloads (Last 6 weeks)6

Reflects downloads up to 19 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Said MBin Faiz RAljaidi MAlshammari M(2024)Comparative analysis of impact of classification algorithms on security and performance bug reportsJournal of Intelligent Systems10.1515/jisys-2024-004533:1Online publication date: 4-Dec-2024
https://doi.org/10.1515/jisys-2024-0045
Du XLi CMa XZheng ZRoychoudhury APaiva AAbreu RStorey M(2024)How Does Pre-trained Language Model Perform on Deep Learning Framework Bug Prediction?Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3643113(346-347)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3643113
Xu YLi Y(2024)A New Method of Security Bug Reports AnalysisIT Professional10.1109/MITP.2023.329852026:2(49-56)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MITP.2023.3298520
Meher JBiswas SMall R(2024)Deep learning-based software bug classificationInformation and Software Technology10.1016/j.infsof.2023.107350166:COnline publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107350
Alraddadi RAlshayeb M(2024)An empirical evaluation of stacked generalization models for binary bug report classificationInnovations in Systems and Software Engineering10.1007/s11334-024-00584-zOnline publication date: 29-Sep-2024
https://doi.org/10.1007/s11334-024-00584-z
Du XLiu ZLi CMa XLi YWang X(2024)LLM-BRC: A large language model-based bug report classification frameworkSoftware Quality Journal10.1007/s11219-024-09675-332:3(985-1005)Online publication date: 24-May-2024
https://doi.org/10.1007/s11219-024-09675-3
Aktas ECakmak EInan MYilmaz CChandra SBlincoe KTonella P(2023)Issue Report Validation in an Industrial ContextProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613887(2026-2031)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3613887
Santos F(2023)Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking SystemsACM SIGSOFT Software Engineering Notes10.1145/3573074.357308848:1(54-58)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3573074.3573088
Santos FPenney JPimentel JWiese ISteinmacher IGerosa M(2023)Tell Me Who Are You Talking to and I Will Tell You What Issues Need Your Skills2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00087(611-623)Online publication date: May-2023
https://doi.org/10.1109/MSR59073.2023.00087
Santos FGrundy J(2023)Skill Recommendation for New Contributors in Open-Source SoftwareProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00084(311-313)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE-Companion58688.2023.00084
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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Classifying Bug Reports to Bugs and Other Requests Using Topic Modeling

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

An Effective Approach for Routing the Bug Reports to the Right Fixers

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations