Relevant bibliographies by topics / AUTOMATIC BUG TRIAGING

Academic literature on the topic 'AUTOMATIC BUG TRIAGING'

Author: Grafiati
Published: 26 October 2023

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Journal articles on the topic "AUTOMATIC BUG TRIAGING"

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Liu, Yong, Xuexin Qi, Jiali Zhang, Hui Li, Xin Ge, and Jun Ai. "Automatic Bug Triaging via Deep Reinforcement Learning." Applied Sciences 12, no. 7 (March 31, 2022): 3565. http://dx.doi.org/10.3390/app12073565.

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Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of bug repair and project progress. However, the large number of bug reports submitted daily increases the triaging workload, and open-source software has a long maintenance cycle. Meanwhile, the developer activity is not stable and changes significantly during software development. Hence, we propose a novel bug triaging model known as auto bug triaging via deep reinforcement learning (BT-RL), which comprises two models: a deep multi-semantic feature (DMSF) fusion model and an online dynamic matching (ODM) model. In the DMSF model, we extract relevant information from bug reports to obtain high-quality feature representation. In the ODM model, through bug report analysis and developer activities, we use a strategy based on the reinforcement learning framework, through which we perform training while learning and recommend developers for bug reports. Extensive experiments on open-source datasets show that the BT-RL method outperforms state-of-the-art methods in bug triaging.
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Banerjee, Sean, Zahid Syed, Jordan Helmick, Mark Culp, Kenneth Ryan, and Bojan Cukic. "Automated triaging of very large bug repositories." Information and Software Technology 89 (September 2017): 1–13. http://dx.doi.org/10.1016/j.infsof.2016.09.006.

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Xia, Xin, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, and Xinyu Wang. "Improving Automated Bug Triaging with Specialized Topic Model." IEEE Transactions on Software Engineering 43, no. 3 (March 1, 2017): 272–97. http://dx.doi.org/10.1109/tse.2016.2576454.

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Jose, Alphy. "An Automated Approach for Mapping Bug Reports to Source Code and Bug Triaging." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 94–101. http://dx.doi.org/10.22214/ijraset.2018.6019.

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Wu, Hongrun, Yutao Ma, Zhenglong Xiang, Chen Yang, and Keqing He. "A spatial–temporal graph neural network framework for automated software bug triaging." Knowledge-Based Systems 241 (April 2022): 108308. http://dx.doi.org/10.1016/j.knosys.2022.108308.

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Zaidi, Syed Farhan Alam, Honguk Woo, and Chan-Gun Lee. "Toward an Effective Bug Triage System Using Transformers to Add New Developers." Journal of Sensors 2022 (April 8, 2022): 1–19. http://dx.doi.org/10.1155/2022/4347004.

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As defects become more widespread in software development and advancement, bug triaging has become imperative for software testing and maintenance. The bug triage process assigns an appropriate developer to a bug report. Many automated and semiautomated systems have been proposed in the last decade, and some recent techniques have provided direction for developing an effective triage system. However, these techniques still require improvement. Another open challenge related to this problem is adding new developers to the existing triage system, which is challenging because the developers have no listed triage history. This paper proposes a transformer-based bug triage system that uses bidirectional encoder representation from transformers (BERT) for word representation. The proposed model can add a new developer to the existing system without building a training model from scratch. To add new developers, we assumed that new developers had a triage history created by a manual triager or human triage manager after learning their skills from the existing developer history. Then, the existing model was fine-tuned to add new developers using the manual triage history. Experiments were conducted using datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was used as a criterion for assessment. The experimental outcome suggests that the proposed triage system is better than other word-embedding-based triage methods for the bug triage problem. Additionally, the proposed method performs the best for adding new developers to an existing bug triage system without requiring retraining using a whole dataset.
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Singla, Heena, Gitika Sharma, and Sumit Sharma. "Domain Specific Automated Triaging System for Bug Classification." Indian Journal of Science and Technology 9, no. 33 (September 16, 2016). http://dx.doi.org/10.17485/ijst/2016/v9i33/97891.

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"Improved Framework for Bug Severity Classification using N-gram Features with Convolution Neural Network." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 1190–96. http://dx.doi.org/10.35940/ijrte.c4292.098319.

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Foreseeing the seriousness/severity of bugs has been established in former research study in order to recover triaging and the process of bug resolution. Therefore, numerous prediction/classification methodologies were developed throughout the years to give an automated reasoning over the seriousness classes. Seriousness or severity is a significant trait of a bug that chooses how rapidly it ought to be measured. It causes designers to comprehend significant bugs on schedule. Though, manual evaluation of severity is a dreary activity and could be off base. This paper comprises of using the text/content mining together along with the use feature selection and bi-grams to improve the order of bugs in six classes. In the proposed methodology the features are refined by the use of convolution layers. Here, the process of convolution-based refining indicates mapping of the features utilizing non-linear methods of all the classes as compared to the existing methodologies.
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Dissertations / Theses on the topic "AUTOMATIC BUG TRIAGING"

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SIROHI, RISHABH. "AN EFFICIENT MACHINE LEARNING TOOL FOR AUTOMATIC BUG TRIAGING." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19839.

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As technology advances at an exponential rate every day, the development and testing teams do their utmost to address problems as soon as they arise in order to meet customer deadlines. Finding the appropriate developer to address a specific bug is typically simple and quick in small organisations, but it can be challenging for large organisations to find the developer who will be able to address the bug quickly, which is one of the main tasks of bug triaging. In this report, we will examine numerous methods for automatically triaging bugs and attempt to identify the optimal method based on a series of research questions that will enable us to understand the statistical analysis of these methods.
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Book chapters on the topic "AUTOMATIC BUG TRIAGING"

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Batista, Arthur, Fabricio D’Morison Marinho, Thiago Rocha, Wilson Oliveira Neto, Giovanni Antonaccio, Tainah Chaves, Diego Falcão, Flávia de S. Santos, Felipe T. Giuntini, and Juliano Efson Sales. "Automated Bug Triaging in a Global Software Development Environment: An Industry Experience." In Natural Language Processing and Information Systems, 160–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08473-7_15.

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Conference papers on the topic "AUTOMATIC BUG TRIAGING"

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Tamrawi, Ahmed, Tung Thanh Nguyen, Jafar Al-Kofahi, and Tien N. Nguyen. "Fuzzy set-based automatic bug triaging." In Proceeding of the 33rd international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1985793.1985934.

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Jain, Vibhor, Anand Rath, and S. Ramaswamy. "Field weighting for automatic bug triaging systems." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6378180.

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Wei, Miaomiao, Shikai Guo, and Rong Chen. "Weighted Data Set Reduction for Automatic Bug Triaging (P)." In The 30th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2018. http://dx.doi.org/10.18293/seke2018-053.

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Chhabra, Deepshikha, Meena Malik, and Sachin Sharma. "Literature survey on automatic bug triaging using machine learning techniques." In INNOVATIONS IN COMPUTATIONAL AND COMPUTER TECHNIQUES: ICACCT-2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108585.

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Sirohi, Rishabh, and Priya Singh. "Automatic Bug Triaging Analysis using Machine Learning Techniques: A Review." In 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2022. http://dx.doi.org/10.1109/icict55121.2022.10064589.

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Ahmed, Iftekhar, Nitin Mohan, and Carlos Jensen. "The Impact of Automatic Crash Reports on Bug Triaging and Development in Mozilla." In OpenSym '14: The International Symposium on Open Collaboration. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2641580.2641585.

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Dedik, Vaclav, and Bruno Rossi. "Automated Bug Triaging in an Industrial Context." In 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2016. http://dx.doi.org/10.1109/seaa.2016.20.

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Gadge, Trupti S., and Nikhil Mangrulkar. "Approaches for automated bug triaging: A review." In 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017. http://dx.doi.org/10.1109/icimia.2017.7975592.

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Dunn, Tim, Natasha Kholgade Banerjee, and Sean Banerjee. "GPU Acceleration of Document Similarity Measures for Automated Bug Triaging." In 2016 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE, 2016. http://dx.doi.org/10.1109/issrew.2016.27.

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Su, Yanqi, Zhenchang Xing, Xin Peng, Xin Xia, Chong Wang, Xiwei Xu, and Liming Zhu. "Reducing Bug Triaging Confusion by Learning from Mistakes with a Bug Tossing Knowledge Graph." In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2021. http://dx.doi.org/10.1109/ase51524.2021.9678574.

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