An Empirical Study on Using Multi-Labels for Issues in GitHub

Author:

Kim JindaeORCID,Lee SeonahORCID

Funder

Basic Science Research Program through the National Research Foundation of Korea

Ministry of Education

Human Resources Development of Korea Institute of Energy Technology Evaluation and Planning

Ministry of Trade, Industry and Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prioritising GitHub Priority Labels;Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering;2024-07-10

2. Accurate Information Type Classification for Software Issue Discussions With Random Oversampling;IEEE Access;2024

3. A Comparison of Pretrained Models for Classifying Issue Reports;IEEE Access;2024

4. GitHub Bug Classification Using Pipeline Approach in Machine Learning;2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI);2023-12-21

5. Understanding and Enhancing Issue Prioritization in GitHub;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

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