GitHub Issue Classification Using BERT-Style Models

Recent innovations in natural language processing techniques have led to the development of various tools for assisting software developers. This paper provides a report of our proposed solution to the issue report classification task from the NL-Based Software Engineering workshop. We approach the task of classifying issues on GitHub repositories using BERT-style models [1, 2, 6, 8]. We propose a neural architecture for the problem that utilizes contextual embeddings for the text content in the GitHub issues. Besides, we design additional features for the classification task. We perform a thorough ablation analysis of the designed features and benchmark various BERT-style models for generating textual embeddings. Our proposed solution performs better than the competition organizer's method and achieves an F-1 score of 0.8653. Our code and trained models are available at https://github.com/Kadam-Tushar/Issue-Classifier.