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GFI-bot: automated good first issue recommendation on GitHub

Published: 09 November 2022 Publication History

Abstract

To facilitate newcomer onboarding, GitHub recommends the use of "good first issue" (GFI) labels to signal issues suitable for newcomers to resolve. However, previous research shows that manually labeled GFIs are scarce and inappropriate, showing a need for automated recommendations. In this paper, we present GFI-Bot (accessible at https://gfibot.io), a proof-of-concept machine learning powered bot for automated GFI recommendation in practice. Project maintainers can configure GFI-Bot to discover and label possible GFIs so that newcomers can easily locate issues for making their first contributions. GFI-Bot also provides a high-quality, up-to-date dataset for advancing GFI recommendation research.

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Cited By

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  • (2025)Software solutions for newcomers’ onboarding in software projects: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107568177(107568)Online publication date: Jan-2025
  • (2024)Enhancing Collaborative Software Development: A Deep Learning Approach for Bot Recommendation2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00180(1366-1375)Online publication date: 2-Jul-2024
  • (2023)Open Source Software Onboarding as a University Course: An Experience ReportProceedings of the 45th International Conference on Software Engineering: Software Engineering Education and Training10.1109/ICSE-SEET58685.2023.00037(324-336)Online publication date: 17-May-2023
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cover image ACM Conferences
ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
November 2022
1822 pages
ISBN:9781450394130
DOI:10.1145/3540250
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 [email protected].

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Publication History

Published: 09 November 2022

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Author Tags

  1. good first issue
  2. onboarding
  3. open-source software
  4. software bot

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  • Research-article

Funding Sources

  • The National Key R&D Program of China Grant
  • the National Natural Science Foundation of China Grant

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ESEC/FSE '22
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Cited By

View all
  • (2025)Software solutions for newcomers’ onboarding in software projects: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2024.107568177(107568)Online publication date: Jan-2025
  • (2024)Enhancing Collaborative Software Development: A Deep Learning Approach for Bot Recommendation2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00180(1366-1375)Online publication date: 2-Jul-2024
  • (2023)Open Source Software Onboarding as a University Course: An Experience ReportProceedings of the 45th International Conference on Software Engineering: Software Engineering Education and Training10.1109/ICSE-SEET58685.2023.00037(324-336)Online publication date: 17-May-2023
  • (2023)Personalized First Issue Recommender for Newcomers in Open Source Projects2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00158(800-812)Online publication date: 11-Sep-2023

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