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The exercise of detecting similar bug reports in bug tracking systems is known as duplicate bug report detection.
Manning, C. D. and Schütze, H., Foundations of Statistical Natural Language Processing. Cambridge, USA: MIT Press, 1999
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P. Runeson, M. Alexandersson and O. Nyholm, ”Detection of Duplicate Defect Reports Using Natural Language Processing,” 29th International Conference on Software Engineering (ICSE’07), 2007, pp. 499-510, doi: 10.1109/ICSE.2007.32
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2008
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A. T. Nguyen, T. T. Nguyen, T. N. Nguyen, D. Lo and C. Sun, ”Duplicate bug report detection with a combination of information retrieval and topic modeling,” 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, 2012, pp. 70-79, doi: 10.1145/2351676.2351687
2012
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J. Lerch and M. Mezini, ”Finding Duplicates of Your Yet Unwritten Bug Report,” 2013 17th European Conference on Software Maintenance and Reengineering, 2013, pp. 69-78, doi: 10.1109/CSMR.2013.17
2013
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A. Alipour, A. Hindle and E. Stroulia, ”A contextual approach towards more accurate duplicate bug report detection,” 2013 10th Working Conference on Mining Software Repositories (MSR), 2013, pp. 183-192, doi: 10.1109/MSR.2013.6624026
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2017
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Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv.org, May 24, 2019
2019
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L. Wang, L. Zhang and J. Jiang, ”Detecting Duplicate Questions in Stack Overflow via Deep Learning Approaches,” 2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019, pp. 506-513, doi: 10.1109/APSEC48747.2019.00074
2019
Cited alongside, same era.
B. S. Neysiani and S. Morteza Babamir, ”Automatic Duplicate Bug Report Detection using Information Retrieval-based versus Machine Learning-based Approaches,” 2020 6th International Conference on Web Research (ICWR), 2020, pp. 288-293, doi: 10.1109/ICWR49608.2020.9122288
H. Isotani, H. Washizaki, Y. Fukazawa, T. Nomoto, S. Ouji and S. Saito, ”Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning,” 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2021, pp. 535-544, doi: 10.1109/ICSME52107.2021.00054
2021
Later among the works it cites.
T. Hirsch and B. Hofer, ”Identifying non-natural language artifacts in bug reports,” 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW), 2021, pp. 191-197, doi: 10.1109/ASEW52652.2021.00046
2021
Later among the works it cites.
B. Kucuk and E. Tuzun, ”Characterizing Duplicate Bugs: An Empirical Analysis,” 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2021, pp. 661-668, doi: 10.1109/SANER50967.2021.00084
2021
Later among the works it cites.
T. M. Rocha and A. L. D. C. Carvalho, ”SiameseQAT: A Semantic Context-Based Duplicate Bug Report Detection Using Replicated Cluster Information,” in IEEE Access, vol. 9, pp. 44610-44630, 2021, doi: 10.1109/ACCESS.2021.3066283
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2020
Cited alongside, same era.
A. Kukkar, R. Mohana, Y. Kumar, A. Nayyar, M. Bilal and K. -S. Kwak, ”Duplicate Bug Report Detection and Classification System Based on Deep Learning Technique,” in IEEE Access, vol. 8, pp. 200749-200763, 2020, doi: 10.1109/ACCESS.2020.3033045
2020
Cited alongside, same era.
H. Mahfoodh and M. Hammad, ”Word2Vec Duplicate Bug Records Identification Prediction Using Tensorflow,” 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), 2020, pp. 1-6, doi: 10.1109/3ICT51146.2020.9311954
2020
Cited alongside, same era.
G. Xiao, X. Du, Y. Sui and T. Yue, ”HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction,” 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 2020, pp. 195-206, doi: 10.1109/ISSRE5003.2020.00027
2020
Cited alongside, same era.
2021
Later among the works it cites.
M. Li and B. -B. Yin, ”ARB-BERT: An Automatic Aging-Related Bug Report Classification Method based on BERT,” 2021 8th International Conference on Dependable Systems and Their Applications (DSA), 2021, pp. 474-483, doi: 10.1109/DSA52907.2021.00071
2021
Later among the works it cites.
Z. Chen, X. Ju, Y. Shen and X. Chen, ”Improving Blocking Bug Pair Prediction via Hybrid Deep Learning,” 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2021, pp. 727-732, doi: 10.1109/QRS-C55045.2021.00110
2021
Later among the works it cites.