BERT-based GitHub issue report classification

Author:

Siddiq Mohammed Latif1,Santos Joanna C. S.1

Affiliation:

1. University of Notre Dame

Publisher

ACM

Reference19 articles.

1. Daniel Artmann. 2020. Applying machine learning algorithms to multi-label text classification on GitHub issues. Daniel Artmann. 2020. Applying machine learning algorithms to multi-label text classification on GitHub issues.

2. Piotr Bojanowski Edouard Grave Armand Joulin and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. arXiv:1607.04606 [cs.CL] Piotr Bojanowski Edouard Grave Armand Joulin and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. arXiv:1607.04606 [cs.CL]

3. GiLA: GitHub label analyzer

4. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018). arXiv:1810.04805 http://arxiv.org/abs/1810.04805 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018). arXiv:1810.04805 http://arxiv.org/abs/1810.04805

5. Investigating the criticality of user‐reported issues through their relations with app rating

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