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arXiv · 2205.01335

Predicting Issue Types with seBERT

Alexander Trautsch·Steffen Herbold
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Abstract

Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type prediction. Our model dominates the baseline fastText for all three issue types in both recall and precisio} to achieve an overall F1-score of 85.7%, which is an increase of 4.1% over the baseline.

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Alexander Trautsch, Steffen Herbold. 2022-05-03. Predicting Issue Types with seBERT. https://arxiv.org/abs/2205.01335

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