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- W4313563755 abstract "With the rapid development of Deep Learning, deep predictive models have been widely applied to improve Software Engineering tasks, such as defect prediction and issue classification, and have achieved remarkable success. They are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the effectiveness of deep predictive models in practice. This problem has become a major obstacle for deep learning-based Software Engineering." @default.
- W4313563755 created "2023-01-06" @default.
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- W4313563755 date "2022-10-10" @default.
- W4313563755 modified "2023-10-10" @default.
- W4313563755 title "Robust Learning of Deep Predictive Models from Noisy and Imbalanced Software Engineering Datasets" @default.
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- W4313563755 doi "https://doi.org/10.1145/3551349.3556941" @default.
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