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

Abstract

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. Description:In this paper, we study whether the pre-trained contextual language models are really better than other previous language models in the label recommendation for the GitHub labels scenario. We try to give some suggestions in fine-tuning pre-trained contextual language representation models. First, we compared four deep learning models, in which three of them use traditional pre-trained word embedding. Furthermore, we compare the performances when using different corpora for pre-training. Results:The experimental results show that: (1) When using large training data, the performance of BERT model is better than other deep learning language models such as Bi-LSTM, CNN and RCNN. While with a small size training data, CNN performs better than BERT. (2) Further pre-training on domain-specific data can indeed improve the performance of models. Conclusions:When recommending labels for issues in GitHub, using pre-trained contextual language representations is better if the training dataset is large enough. Moreover, we discuss the experimental results and provide some implications to improve label recommendation performance for GitHub issues.