The findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap: communities often default to low-effort defensive strategies that protect short-term review capacity while making openness difficult to sustain.
Abstract
Open source software (OSS) communities are facing increasing pressure from Generative AI (GenAI) tools. We call it AI-DDoS: a denial-of-service effect in which plausible but low-quality AI-generated contributions overwhelm OSS community capacity. Using a phenomenon-based mixed-methods approach, we first analyze practitioner accounts from Reddit, OSS mentor mailing lists, and blogs to identify six recurring themes and derive hypotheses. We then evaluate these hypotheses using Bayesian Structural Time Series analysis across 294 repositories with over 2 million pull requests and issues. Our results show that while PR volume increased in 2025, merge rates declined, with one-time contributors experiencing an 18.18% drop in PR merge rates relative to the counterfactual. Finally, we identify 11 remediation strategies through practitioners'interviews and validate them with a survey of 229 OSS practitioners, grouping them into preservative, adaptive, and transformative orientations. Our findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap: communities often default to low-effort defensive strategies that protect short-term review capacity while making openness difficult to sustain.
The AI Contribution Governance Framework is introduced, which organizes recurring concerns and governance mechanisms across projects and helps OSS communities develop AI contribution policies and provides researchers with a vocabulary for studying how AI is changing collaborative software production.
Interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023 offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
Benyamin T. Tabarsi, Heidi Reichert, Sam Gilson et al.· Empirical Software Engineeri...· 22 citations· ⚡1
A human-centered mitigation framework that treats information quality and the equitable distribution of verification labor as shared institutional responsibilities is contributed — advancing sustainable, ethical, and future-oriented information management in the generative AI era.
Blenda G. Mutuma· Communications of the IIMA· 0 citations
The first large-scale empirical study of AI governance policies in OSS is presented, identifying 385 projects that adopted AI policies and derive TRACE, a framework capturing five governance dimensions: Transparency, Responsibility, Attribution, Constraints, and Enforcement.
Yun-Qi Chen, Thomas Zimmermann, Bianca Trinkenreich· 1 citation
This work analyzes 281 AI contribution policies and identifies ten countermeasures against AI slop, targeting pull requests, users, and autonomous agents, to give maintainers and researchers a baseline and a labeled corpus for studying the impact of AI policies.
André C. Hora, Romain Robbes, Stefano Zacchiroli· 0 citations
A small core retains implementation authority while a broader community continues to shape the software without writing code, and access to coding increasingly depends on approval rather than self-initiated contribution, forming a stewardship community.
G. Robles, D. German· 0 citations
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