Mitigating the impact of mislabeled data on deep predictive models: an empirical study of learning with noise approaches in software engineering tasks

Author:

Shen Jian,Li Zhong,Lu Yifei,Pan Minxue,Li Xuandong

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Reference87 articles.

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3. Antoniol, G., Ayari, K., Penta, M.D., Khomh, F., Guéhéneuc, Y.: Is it a bug or an enhancement?: a text-based approach to classify change requests. In: Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, CASCON 2018, Markham, Ontario, Canada, October 29–31, 2018, pp. 2–16 (2018)

4. Cabral, G.G., Minku, L.L., Shihab, E., Mujahid, S.: Class imbalance evolution and verification latency in just-in-time software defect prediction. In: Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25–31, 2019, pp. 666–676 (2019)

5. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

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