Evaluating Deep Learning Methods For Classifying Bugs
DOI:
https://doi.org/10.32628/IJSRST523102143Keywords:
Software maintenance, Software development, Outdated software, Unreliable software, Security threats ,Bug reports, Bug resolution, Managerial responsibility, Developer efficiency, Bug assignment, Managerial capacity, Progress pace, Developer understanding, Bug management, Team managementAbstract
Software maintenance is a crucial part of software development, especially now more than ever. Without proper maintenance, software can become outdated and unreliable. and vulnerable to security threats, which can have serious consequences for users and organisations that rely on it. But as the software projects become larger, it becomes It is the responsibility of the managers to assign the bugs to the developers so that the developers can use their time efficiently in resolving those bugs. But the capacity of Managers’ ability to analyse each and every bug report and assign it to the appropriate developer is being out- paced by the shear number of bug reports, leading to slow progress. Its impossible for developers or managers to be able to understand hundreds of reports a week, let alone being able to have a good idea of each and every developer in the team to be able to appropriately assign the bugs to them.
References
- S. Mani, A. Sankaran, and R. Aralikatte, Deeptriage: Exploring the effectiveness of deep learning for bug triaging, Corr, vol. abs/1801.01275, 2018, Available: http://arxiv.org/abs/1801.01275
- R. R. Panda and N. K. Nagwani, Classification and in- tuitionistic fuzzy set based software bug triaging tech- niques, Journal of king saud university - computer and information sciences, vol. 34, no. 8, Part B, pp. 63036323, 2022, doi: https://doi.org/https:// doi.org/10.1016/j.jksuci.2022.01.020.
- A. Tamrawi, T. T. Nguyen, J. M. Al-Kofahi, and T. N. Nguyen, Fuzzy set and cache-based approach for bug triaging, in Proceedings of the 19th acm sigsoft sympo- sium and the 13th european conference on foundations of software engineering, in Esec/fse 11. Szeged, Hun- gary: Association for Computing Machinery, 2011, pp. 365375. doi: 10.1145/2025113.2025163.
- D. Cubranic and G. C. Murphy, Automatic bug triage using text categorization, in International conference on software engineering and knowledge engineering, 2004.
- J. Xuan, H. Jiang, Z. Ren, J. Yan, and Z. Luo, Auto- matic bug triage using semi-supervised text classifica- tion, Arxiv, vol. abs/1704.04769, 2017.
- X. Xia, D. Lo, X. Wang, and B. Zhou, Accurate de- veloper recommendation for bug resolution, in Pro- ceedings - 20th working conference on reverse engi- neering, wcre 2013, in Proceedings - working con- ference on reverse engineering, wcre. United States of America: IEEE, Institute of Electrical and Elec- tronics Engineers, Dec. 2013, pp. 7281. doi: 10.1109/WCRE.2013.6671282.
- G. Jeong, S. Kim, and T. Zimmermann, Improving bug triage with bug tossing graphs, in Esec/fse 09, 2009.
- P. Bhattacharya and I. Neamtiu, Fine-grained incre- mental learning and multi-feature tossing graphs to im- prove bug triaging, 2010 ieee international conference on software maintenance, pp. 110, 2010.
- W. Wu, W. Zhang, Y. Yang, and Q. Wang, Drex: Devel- oper recommendation with k-nearest-neighbor search and expertise ranking, 2011 18th asia-pacific software engineering conference, pp. 389396, 2011.
- D. Matter, A. Kuhn, and O. Nierstrasz, Assigning bug reports using a vocabulary-based expertise model of developers, in 2009 6th ieee international working conference on mining software repositories, 2009, pp. 131140. doi: 10.1109/MSR.2009.5069491.
- F. Servant and J. A. Jones, Whosefault: Automatic developer-to-fault assignment through fault localiza- tion, 2012 34th international conference on software engineering (icse), pp. 3646, 2012.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST
This work is licensed under a Creative Commons Attribution 4.0 International License.