GitHub Issue Classification Using BERT-Style Models

Bharadwaj, S and Kadam, T (2022) GitHub Issue Classification Using BERT-Style Models. In: 1st IEEE/ACM International Workshop on Natural Language-Based Software Engineering, NLBSE 2022, 8 May 2022, Pittsburgh, pp. 40-43.

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Abstract

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 F1 score of 0.8653. Our code and trained models are available at https://github.com/Kadam-Tushar/Issue-Classifier.

Item Type:	Conference Paper
Publication:	Proceedings - 1st International Workshop on Natural Language-Based Software Engineering, NLBSE 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Natural language processing systems; Software engineering, Classification tasks; Computing methodologies; Computing methodology ? natural language processing; Embeddings; Engineering software; Language processing; Language processing techniques; Natural languages; Software and its engineering ? software maintenance tool; Software maintenance tools, Embeddings
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	23 Aug 2022 05:40
Last Modified:	23 Aug 2022 05:40
URI:	https://eprints.iisc.ac.in/id/eprint/76172

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