Abstract
Classifying bugs in software systems indeed often involves considering factors like severity, complexity and reproducibility. More elusive and troublesome types of bugs in software development are Mandelbugs which exhibit characteristics of being both complex and non-deterministic, making them exceptionally challenging to reproduce and resolve. However, developers can perform a quick, inexpensive yet most effective methods to identify Mandelbug root causes, and design targeted fault-tolerance mechanisms to enhance system reliability and resilience. His work studied the distribution of Mandelbugs and proposed a classification engine – machine learning, feature engineering, transfer learning and natural language processing (NLP) approach to quickly and effectively categorize Mandelbugs. We evaluated our proposed solution by extracting and processing the text descriptions of Mandelbugs obtained from four different datasets which has 210 Mandelbug records. Our performance evaluation revealed that use of transfer-learning approach has improved F1-scores as well as accuracy (30% - 65%) when compared to that of baseline classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Grottke, M., Nikora, A., Trivedi, K.: An empirical investigation of fault types in space mission system software (2010)
Grottke, M., Trıvedı, K.S.: Software faults, software aging and software rejuvenation special survey new development of software reliability engineering. J. Reliab. Eng. Assoc. Japan 27(7), 425–438 (2005)
Grottke, M., Trivedi, K.: A classification of software faults. In: Supplemental Proc. Sixteenth International Symposium on Software Reliability Engineering, vol. 27, pp. 4.19–4.20 (2005)
Grottke, M., Trivedi, K.S.: Fighting bugs: remove, retry, replicate, and rejuvenate. Computer 40, 107–109 (2007)
Cotroneo, D., Grottke, M., Natella, R., Pietrantuono, R., Trivedi, K.S.: Fault triggers in open-source software: an experience report. In: 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE), pp. 178–187 (2013)
Grottke, M., Kim, D.S., Mansharamani, R., Nambiar, M., Natella, R., Trivedi, K.S.: Recovery from software failures caused by mandelbugs. IEEE Trans. Reliab. 65, 70–87 (2016)
Cotroneo, D., Iannillo, A.K., Natella, R., Pietrantuono, R., Russo, S.: The software aging and rejuvenation, pp. 108–113 (2015) repository. http://openscience.us/repo/software-aging/
Gegick, M., Rotella, P., Xie, T.: Identifying security bug reports via text mining: An industrial case study. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 11–20 (2010)
Arshad, F.A., Krause, R.J., Bagchi, S.: Characterizing configuration problems in java ee application servers: an empirical study with glassfish and jboss. In: 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE), (Los Alamitos, CA, USA), pp. 198–207, IEEE Computer Society (2013)
Zaman, S., Adams, B., Hassan, A.E.: Security versus performance bugs: A case study on firefox. In: Proceedings of the 8th Working Conference on Mining Software Repositories, MSR ’11, New York, NY, USA, pp. 93–102. Association for Computing Machinery (2011)
Xia, X., Zhou, X., Lo, D., Zhao, X.: An empirical study of bugs in software build systems. In: 2013 13th International Conference on Quality Software, pp. 200–203 (2013)
Thung, F., Lo, D., Jiang, L.: Automatic defect categorization. In: 2012 19th Working Conference on Reverse Engineering, pp. 205–214 (2012)
Xia, X., Lo, D., Qiu, W., Wang, X., Zhou, B.: Automated configuration bug report prediction using text mining. In: 2014 IEEE 38th Annual Computer Software and Applications Conference, pp. 107–116 (2014)
Wu, R., Zhang, H., Kim, S., Cheung, S.C.: Relink: Recovering links between bugs and changes. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE ’11, (New York, NY, USA), pp. 15–25, Association for Computing Machinery (2011)
Sun, C., Lo, D., Wang, X., Jiang, J., Khoo, S.: A discriminative model approach for accurate duplicate bug report retrieval. In: 2010 ACM/IEEE 32nd International Conference on Software Engineering, vol. 1, pp. 45–54 (2010)
Sun, C., Lo, D., Khoo, S., Jiang, J.: Towards more accurate retrieval of duplicate bug reports. In: 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp. 253–262 (2011)
Nguyen, A.T., Nguyen, T.T., Nguyen, T.N., Lo, D., Sun, C.: Duplicate bug report detection with a combination of information re- trieval and topic modeling. In: 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, pp. 70– 79 (2012)
Marcus, A., Maletic, J.I.: Recovering documentation-to-source-code traceability links using latent semantic indexing. In: Proceedings of the 25th International Conference on Software Engineering, ICSE ’03, (USA), pp. 125–135, IEEE Computer Society (2003)
Haiduc, S., Aponte, J., Marcus, A.: Supporting program compre- hension with source code summarization. In: ACM/IEEE 32nd International Conference on Software Engineering, vol. 2, pp. 223–226 (2010)
Zhou, J., Zhang, H., Lo, D.: Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports. In: 2012 34th International Conference on Software Engineering (ICSE), pp. 14–24 (2012)
Zhao, J., Shetty, S., Pan, J.W.: Feature-based transfer learning for network security. In: IEEE Military Communications Conference (MILCOM), pp. 17–22 (2017)
Natural Language Toolkit (NLTK). Available online: https://www.nltk.org/
Acknowledgements
This work was partly supported by Targeted Infusion Project: Cybersecurity for Everybody - A Multi-Tier Approach to Cybersecurity Education, Training, and Awareness in the Undergraduate Curriculum by the National Science Foundation (NSF award #1912284).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Biswal, B. (2024). Mandelbug Classification Engine: Transfer Learning and NLP Approach. In: Dehuri, S., Cho, SB., Padhy, V.P., Shanmugam, P., Ghosh, A. (eds) Machine Intelligence, Tools, and Applications. ICMITA 2024. Learning and Analytics in Intelligent Systems, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-031-65392-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-65392-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65391-9
Online ISBN: 978-3-031-65392-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)