How well do pre-trained contextual language representations recommend labels for GitHub issues?

摘要

Motivation:Open-source organizations use issues to collect user feedback, software bugs, and feature requests in GitHub. Many issues do not have labels, which makes labeling time-consuming work for the maintainers. Recently, some researchers used deep learning to improve the performance of automated tagging for software objects. However, these researches use static pre-trained word vectors that cannot represent the semantics of the same word in different contexts. Pre-trained contextual language representations have been shown to achieve outstanding performance on lots of NLP tasks.