Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

Abstract

Various issues or bugs are reported during the software development. It takes considerable effort, time, and cost for the software developers to triage these issues manually. Many previous studies have proposed various method to automate the triage process by predicting component using word-based language models. However, these methods still suffer from unsatisfactory performance due to their structural limitations and ignorance of the word context. In this paper, we propose a novel technique based on pretrained language models and it aims to predict a component of an issue report. Our approach fine-tunes the pretrained language models to conduct multilabel classifications. The proposed approach outperforms the previous state-of-the-art method by more than 30% with respect to the recall at k on all the datasets considered in our experiment. This improvement suggests that fine-tuned pretrained language models can help us to predict issue components effectively.