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Authors: Giuseppe Colavito, Filippo Lanubile, Nicole Novielli, and Luigi Quaranta
MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories
April 2024
Pages 469 - 480
Published: 02 July 2024 Publication History
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Abstract
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, achieving state-of-the-art performance. More recently, decoder-only models such as GPT have become prominent in SE research due to their surprising capabilities to achieve state-of-the-art performance even for tasks they have not been trained for. To the best of our knowledge, GPT-like models have not been applied yet to the problem of issue classification, despite the promising results achieved for many other software engineering tasks. In this paper, we investigate to what extent we can leverage GPT-like LLMs to automate the issue labeling task. Our results demonstrate the ability of GPT-like models to correctly classify issue reports in the absence of labeled data that would be required to fine-tune BERT-like LLMs.
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Index Terms
Leveraging GPT-like LLMs to Automate Issue Labeling
Information systems
Information retrieval
Retrieval tasks and goals
Clustering and classification
Software and its engineering
Software creation and management
Software post-development issues
Documentation
Maintaining software
Software evolution
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Published In
MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories
April 2024
788 pages
ISBN:9798400705878
DOI:10.1145/3643991
- Chair:
- Diomidis Spinellis,
- Program Chair:
- Alberto Bacchelli,
- Program Co-chair:
- Eleni Constantinou
Copyright © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
Sponsors
- SIGSOFT: ACM Special Interest Group on Software Engineering
- IEEE CS
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 July 2024
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Author Tags
- LLM
- issue labeling
- GPT
- software maintenance and evolution
- labeling unstructured data
Qualifiers
- Research-article
Conference
MSR '24
Sponsor:
- SIGSOFT
MSR '24: 21st International Conference on Mining Software Repositories
April 15 - 16, 2024
Lisbon, Portugal
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