Our paper, “BERT-Based GitHub Issue Report Classification”, got accepted for The 1st Intl. Workshop on Natural Language-based Software Engineering (NLBSE’22) co-located with ICSE 2022 in the tool competition. we describe a BERT-based classification technique to automatically label issues as questions, bugs, or enhancements. We evaluate our approach using a dataset containing over 800,000 labeled issues from real open source projects available on GitHub. Our approach classified reported issues with an average F1-score of 0.8571. Our technique outperforms a previous machine learning technique based on FastText.
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