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The Multifaceted Nature of Mentoring in OSS: Strategies, Qualities, and Ideal Outcomes
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“This two-week survey followed established SE research practices [8, 10, 11].”
From The Fast and Spurious: Developer Productivity with GenAI (2025) · cites this paper as [10]
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“Providing Task Resources Learners benefit resources (blue polygon) that enable them to solve problems by themselves, ultimately building their confidence and competence [32].”
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“Designing an AI Mentor For OSS/SE We believe future research could leverage strategies to design AI-driven interventions, such as chatbots integrated with large language models [41].”
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Cited by
Bhuiyan, M. H. M., Kumar, M. K. B., & Staicu, C.-A. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3787766 · Cited by 5
You can contribute if you... An Empirical Framework of AI Contribution Policies in OSS
Robles, G., & German, D. M. (2026) · arXiv · doi:10.48550/arxiv.2609.07919
Metadata
- Authors
- Feng, Z., Steinmacher, I., Gerosa, M., Menezes, T., Serebrenik, A., Milewicz, R., & Sarma, A.
- Affiliations
- Oregon State University,USA
- Northern Arizona University,USA
- CodeDay,USA
- Eindhoven University of Technology,Netherlands
- Sandia National Laboratories,USA
- Venue
- 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE)
- Publisher
- IEEE
- Published
- 2025-04-27
- Pages
- 203-214
- Cited by
- 6
- References
- 5
- Funders
- NSF (1901031,2235601,2236198,2247929,2303042,2303043,2347311)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.