From First Patch to Long-Term Contributor: Evaluating Onboarding Recommendations for OSS Newcomers

Attracting and retaining a steady stream of new contributors is crucial to ensuring the long-term survival of open-source software (OSS) projects. However, there are two key research gaps regarding recommendations for onboarding new contributors to OSS projects. First, most of the existing recommendations are based on a limited number of projects, which raises concerns about their generalizability. If a recommendation yields conflicting results in a different context, it could hinder a newcomer's onboarding process rather than help them. Second, it's unclear whether these recommendations also apply to experienced contributors. If certain recommendations are specific to newcomers, continuing to follow them after their initial contributions are accepted could hinder their chances of becoming long-term contributors. To address these gaps, we conducted a two-stage mixed-method study. In the first stage, we conducted a Systematic Literature Review (SLR) and identified 15 task-related actionable recommendations that newcomers to OSS projects can follow to improve their odds of successful onboarding. In the second stage, we conduct a large-scale empirical study of five Gerrit-based projects and 1,155 OSS projects from GitHub to assess whether those recommendations assist newcomers’ successful onboarding. Our results suggest that four recommendations positively correlate with newcomers’ first patch acceptance in most contexts. Four recommendations are context-dependent, and four indicate significant negative associations for most projects. Our results also found three newcomer-specific recommendations, which OSS joiners should abandon at non-newcomer status to increase their odds of becoming long-term contributors.

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Publication Details

Journal
IEEE Transactions on Software Engineering
Published
2025-03-13
DOI
https://doi.org/10.1109/tse.2025.3550881
Citations
1
Primary Topic
Web Data Mining and Analysis
Type
article
Field-Weighted Citation Impact
3.25
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article

From First Patch to Long-Term Contributor: Evaluating Onboarding Recommendations for OSS Newcomers

Amiangshu Bosu, Sayma Sultana, Asif Kamal Turzo
1 citations
IEEE Transactions on Software Engineering
Web Data Mining and Analysis
3.25
article

From First Patch to Long-Term Contributor: Evaluating Onboarding Recommendations for OSS Newcomers

Amiangshu Bosu, Sayma Sultana, Asif Kamal Turzo
article en
1 citations

Abstract

Attracting and retaining a steady stream of new contributors is crucial to ensuring the long-term survival of open-source software (OSS) projects. However, there are two key research gaps regarding recommendations for onboarding new contributors to OSS projects. First, most of the existing recommendations are based on a limited number of projects, which raises concerns about their generalizability. If a recommendation yields conflicting results in a different context, it could hinder a newcomer's onboarding process rather than help them. Second, it's unclear whether these recommendations also apply to experienced contributors. If certain recommendations are specific to newcomers, continuing to follow them after their initial contributions are accepted could hinder their chances of becoming long-term contributors. To address these gaps, we conducted a two-stage mixed-method study. In the first stage, we conducted a Systematic Literature Review (SLR) and identified 15 task-related actionable recommendations that newcomers to OSS projects can follow to improve their odds of successful onboarding. In the second stage, we conduct a large-scale empirical study of five Gerrit-based projects and 1,155 OSS projects from GitHub to assess whether those recommendations assist newcomers’ successful onboarding. Our results suggest that four recommendations positively correlate with newcomers’ first patch acceptance in most contexts. Four recommendations are context-dependent, and four indicate significant negative associations for most projects. Our results also found three newcomer-specific recommendations, which OSS joiners should abandon at non-newcomer status to increase their odds of becoming long-term contributors.

IEEE Transactions on Software EngineeringVol. 51(4)
Wayne State University (US)
Openalex Percentile: Top 10%
Web Data Mining and Analysis
3.25
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