An Empirical Study on Noisy Label Learning for Program Understanding

Wenhan Wang, Yanzhou Li, Anran Li, Jian Zhang, Wei Ma, Yang Liu

Abstract

Recently, deep learning models have been widely applied in program understanding tasks, and these models achieve state-of-the-art results on many benchmark datasets. A major challenge of deep learning for program understanding is that the effectiveness of these approaches depends on the quality of their datasets, and these datasets often contain noisy data samples. A typical kind of noise in program understanding datasets is label noise, which means that the target outputs for some inputs are incorrect.

BibTeX

@inproceedings{Wang-al:ICSE24,
  author    = {Wenhan Wang and
               Yanzhou Li and
               Anran Li and
               Jian Zhang and
               Wei Ma and
               Yang Liu},
  title     = {An Empirical Study on Noisy Label Learning for Program Understanding},
  booktitle = {ICSE},
  pages     = {95:1--95:12},
  publisher = {{ACM}},
  year      = {2024},
}

An Empirical Study on Noisy Label Learning for Program Understanding

Abstract

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