Leveraging GPT-like LLMs to Automate Issue Labeling

Issue labeling is a crucial task for the effective management of software projects. To date, several approaches have been put forth for the automatic assignment of labels to issue reports. In particular, supervised approaches based on the fine-tuning of BERT-like language models have been proposed,...

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Bibliographic Details

Published in:	Proceedings (IEEE/ACM International Conference on Mining Software Repositories. Online) pp. 469 - 480
Main Authors:	Colavito, Giuseppe, Lanubile, Filippo, Novielli, Nicole, Quaranta, Luigi
Format:	Conference Proceeding
Language:	English
Published:	ACM 15.04.2024
Subjects:	Annotations Computational modeling Data models GPT Issue Labeling Labeling Unstructured Data LLM Manuals Scalability Software Software Maintenance and Evolution Supervised learning
ISSN:	2574-3864
Online Access:	Get full text
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