skip to main content
10.1145/3019612.3019788acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Analyzing emotion words to predict severity of software bugs: a case study of open source projects

Published: 03 April 2017 Publication History

Abstract

A successful software development project becomes an essential part of a software company's reputation. Thus, lots of project managers focus more on maintenance than on other management processes. Previous works studied how to help the maintenance process by detecting bug duplication and predicting the severity of bugs. This paper continues that kind of special work by analyzing emotion words for bug-severity prediction. In detail, we construct an emotion words-based dictionary for verifying bug reports' textual emotion analyses based on positive and negative terms. Then, we modify a machine learning algorithm, the Naïve Bayes multinomial, calling the new algorithm EWD-Multinomial. We compare this EWD-Multinomial study with our baselines, including Naïve Bayes multinomial and a Lamkanfi study, for open source projects such as Eclipse, Android, and JBoss. The result shows this study's algorithm outperforms the others.

References

[1]
Zimmermann, T., Premraj, R., Sillito, J., and Breu, S, "Improving bug tracking systems". In Proc. ICSE Companion, pp. 247--250, 2009.
[2]
Zhang, T., Yang, G., Lee, B., and Chan, A. T., "Predicting severity of bug report by mining bug repository with concept profile", In Proc. of the 30th Annual ACM Symposium on Applied Computing, pp. 1553--1558, 2015.
[3]
Zhang, T., Chen, J., Yang, G., Lee, B., and Luo, X., "Towards more accurate severity prediction and fixer recommendation of software bugs", Journal of Systems and Software, Vol. 117, pp. 166--184, 2016.
[4]
Yang, G., Zhang, T., and Lee, B., "Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports", In Proc. Computer software and applications conference, pp. 97--106, 2014.
[5]
Lamkanfi, A., Demeyer, S., Soetens, Q. D., and Verdonck, T., "Comparing mining algorithms for predicting the severity of a reported bug", In Proc. Software Maintenance and Reengineering, pp. 249--258, 2011.
[6]
Baccianella, S., Esuli, A., and Sebastiani, F., "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining", In LREC, Vol. 10, pp. 2200--2204, 2010.
[7]
https://code.google.com/p/android/issues/detail?can=2&q=81613&id=81613
[8]
Eclipse, https://bugs.eclipse.org/bugs/
[9]
Android, https://code.google.com/p/android/issues/list
[10]
JBoss, https://issues.jboss.org/
[11]
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bernard, S., and McClosky, D., "The Stanford CoreNLP Natural Language Processing Toolkit", In Proc. ACL (System Demonstrations), pp. 55--60, 2014.
[12]
Goutte, C., and Gaussier, E., "A probabilistic interpretation of precision, recall and F-score, with implication for evaluation", In Proc. European Conference on Information Retrieval, pp. 345--359, 2005.
[13]
Kohavi, R., "A study of cross-validation and bootstrap for accuracy estimation and model selection", In Proc. of the 14th international joint conference on Artificial intelligence, Vol. 14, No. 2, pp. 1137--1145, 1995.
[14]
Lamkanfi, A., Demeyer, S., Giger, E., and Goethals, B., "Predicting the severity of a reported bug", In Proc. 7th IEEE Working Conference on Mining Software Repositories, pp. 1--10, 2010.
[15]
Wilcoxon, F., "Individual comparisons by ranking methods", Biometrics bulletin, Vol. 1, No. 6, pp. 80--83, 1945.
[16]
The T-Test, "Research Methods Knowledge Base," http://www.socialresearchmethods.net/kb/contents.php.
[17]
Shapiro, S. S., and Wilk, M. B., "An analysis of variance test for normality (complete samples)", In Biometrika, Vol. 52, No. 3/4, pp. 591--611, 1965.
[18]
Team, R. C., "R: A language and environment for statistical computing", 2013.
[19]
Yang, C. Z., Hou, C. C., Kao, W. C., and Chen, X., "An empirical study on improving severity prediction of defect reports using feature selection", In Proc. 19th Asia-Pacific Software Engineering Conference, pp. 240--249, 2012.
[20]
Tian, Y., Lo, D., and Sun, C., "Information retrieval based nearest neighbor classification for fine-grained bug severity prediction", In Proc. 19th Working Conference on Reverse Engineering, pp. 215--224, 2012.
[21]
Menzies, T., and Marcus, A., "Automated severity assessment of software defect reports", In Proc. IEEE International Conference on Software Maintenance, pp. 346--355, 2008.
[22]
LeCun, Y., Bengio, Y., and Hinton, G., "Deep learning", Nature, 521(7553), pp. 436--444, 2015.
[23]
Xuan, J., Jiang, H., Ren, Z., and Zou, W, "Developer prioritization in bug repositories", In Proc. 34th International Conference on Software Engineering, pp. 25--35, 2012.
[24]
Xuan, J., Jiang, H., Hu, Y., Ren, Z., Zou, W., Luo, Z., and Wu, X, "Towards effective bug triage with software data reduction techniques", IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 1, pp. 264--280, 2015.

Cited By

View all
  • (2024)BERT based severity prediction of bug reports for the maintenance of mobile applicationsJournal of Systems and Software10.1016/j.jss.2023.111898208:COnline publication date: 1-Feb-2024
  • (2023)ADPTriage: Approximate Dynamic Programming for Bug TriageIEEE Transactions on Software Engineering10.1109/TSE.2023.330724349:10(4594-4609)Online publication date: 1-Oct-2023
  • (2023)A Framework for Emotion-Oriented Requirements Change Handling in Agile Software EngineeringIEEE Transactions on Software Engineering10.1109/TSE.2023.325314549:5(3325-3343)Online publication date: 1-May-2023
  • Show More Cited By

Index Terms

  1. Analyzing emotion words to predict severity of software bugs: a case study of open source projects

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SAC '17: Proceedings of the Symposium on Applied Computing
    April 2017
    2004 pages
    ISBN:9781450344869
    DOI:10.1145/3019612
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 April 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. bug report
    2. bug severity prediction
    3. emotion words-based dictionary
    4. software maintenance

    Qualifiers

    • Research-article

    Funding Sources

    • Ministry of Science, ICT & Future Planning

    Conference

    SAC 2017
    Sponsor:
    SAC 2017: Symposium on Applied Computing
    April 3 - 7, 2017
    Marrakech, Morocco

    Acceptance Rates

    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

    Upcoming Conference

    SAC '25
    The 40th ACM/SIGAPP Symposium on Applied Computing
    March 31 - April 4, 2025
    Catania , Italy

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)22
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 16 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)BERT based severity prediction of bug reports for the maintenance of mobile applicationsJournal of Systems and Software10.1016/j.jss.2023.111898208:COnline publication date: 1-Feb-2024
    • (2023)ADPTriage: Approximate Dynamic Programming for Bug TriageIEEE Transactions on Software Engineering10.1109/TSE.2023.330724349:10(4594-4609)Online publication date: 1-Oct-2023
    • (2023)A Framework for Emotion-Oriented Requirements Change Handling in Agile Software EngineeringIEEE Transactions on Software Engineering10.1109/TSE.2023.325314549:5(3325-3343)Online publication date: 1-May-2023
    • (2022)Learning to transfer knowledge from RDF Graphs with gated recurrent unitsIntelligent Data Analysis10.3233/IDA-21591926:3(679-694)Online publication date: 1-Jan-2022
    • (2022)Predicting Risk Matrix in Software Development Projects using BERT and K-Means2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)10.23919/EECSI56542.2022.9946637(137-142)Online publication date: 6-Oct-2022
    • (2022)Severity Prediction for Bug Reports using Tree-based Ensemble Models: A Comparative Study2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI55746.2022.9832212(01-05)Online publication date: 10-Jun-2022
    • (2022)CIL-BSP: Bug Report Severity Prediction based on Class Imbalanced Learning2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C57518.2022.00051(298-306)Online publication date: Dec-2022
    • (2022)Criticism of the Risk Management Process in Scrum Methodology2022 International Conference on Electrical and Information Technology (IEIT)10.1109/IEIT56384.2022.9967893(338-343)Online publication date: 15-Sep-2022
    • (2022)Bug Severity Prediction Algorithm Using Topic-Based Feature Selection and CNN-LSTM AlgorithmIEEE Access10.1109/ACCESS.2022.320468910(94643-94651)Online publication date: 2022
    • (2022)An Extendable Sentiment Monitoring Model for SNS Considering Environmental FactorsSocial Computing and Social Media: Design, User Experience and Impact10.1007/978-3-031-05061-9_29(408-421)Online publication date: 16-Jun-2022
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media