Revolutionizing Newcomers’ Onboarding Process in OSS Communities: The Future AI Mentor

Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are often hindered by experts' busy schedules. The recent rapid advancements of AI in software engineering have brought opportunities to leverage AI as a substitute for expert mentoring. However, the potential role of AI as a comprehensive mentor throughout the entire onboarding process remains unexplored. To identify design strategies of this "AI mentor", we applied Design Fiction as a participatory method with 19 OSS newcomers. We investigated their current onboarding experience and elicited 32 design strategies for future AI mentor. Participants envisioned AI mentor being integrated into OSS platforms like GitHub, where it could offer assistance to newcomers, such as "recommending projects based on personalized requirements" and "assessing and categorizing project issues by difficulty". We also collected participants' perceptions of a prototype, named "OSSerCopilot", that implemented the envisioned strategies. They found the interface useful and user-friendly, showing a willingness to use it in the future, which suggests the design strategies are effective. Finally, in order to identify the gaps between our design strategies and current research, we conducted a comprehensive literature review, evaluating the extent of existing research support for this concept. We find that research is relatively scarce in certain areas where newcomers highly anticipate AI mentor assistance, such as "discovering an interested project". Our study has the potential to revolutionize the current newcomer-expert mentorship and provides valuable insights for researchers and tool designers aiming to develop and enhance AI mentor systems.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-19
DOI
https://doi.org/10.1145/3715767
Citations
2
Primary Topic
Open Source Software Innovations
Type
article
Field-Weighted Citation Impact
3.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Revolutionizing Newcomers’ Onboarding Process in OSS Communities: The Future AI Mentor

Lin Shi, Xin Tan, Yinghao Zhu, Xiaoli Lian et al.
2 citations
Proceedings of the ACM on software engineering.
Open Source Software Innovations
3.39
article

Revolutionizing Newcomers’ Onboarding Process in OSS Communities: The Future AI Mentor

Lin Shi, Xin Tan, Yinghao Zhu, Xiaoli Lian, Xiao Long, Li Zhang
article en
2 citations

Abstract

Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are often hindered by experts' busy schedules. The recent rapid advancements of AI in software engineering have brought opportunities to leverage AI as a substitute for expert mentoring. However, the potential role of AI as a comprehensive mentor throughout the entire onboarding process remains unexplored. To identify design strategies of this "AI mentor", we applied Design Fiction as a participatory method with 19 OSS newcomers. We investigated their current onboarding experience and elicited 32 design strategies for future AI mentor. Participants envisioned AI mentor being integrated into OSS platforms like GitHub, where it could offer assistance to newcomers, such as "recommending projects based on personalized requirements" and "assessing and categorizing project issues by difficulty". We also collected participants' perceptions of a prototype, named "OSSerCopilot", that implemented the envisioned strategies. They found the interface useful and user-friendly, showing a willingness to use it in the future, which suggests the design strategies are effective. Finally, in order to identify the gaps between our design strategies and current research, we conducted a comprehensive literature review, evaluating the extent of existing research support for this concept. We find that research is relatively scarce in certain areas where newcomers highly anticipate AI mentor assistance, such as "discovering an interested project". Our study has the potential to revolutionize the current newcomer-expert mentorship and provides valuable insights for researchers and tool designers aiming to develop and enhance AI mentor systems.

Proceedings of the ACM on software engineering.Vol. 2(FSE)
Beihang University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 7%
Open Source Software Innovations
3.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.