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.
Abstract
Human-centric AI for software engineering means keeping humans responsible for work performed with AI. In Open Source Software (OSS), AI lowers the cost of implementing changes, but reviewing someone else's contribution remains comparatively expensive, so some projects now restrict who may contribute implementations while still welcoming other participation---not because the code is AI-generated, but because it no longer justifies the review cost. We call the resulting form a stewardship community: 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. This raises a broader question: what happens to the human community when coding agents let maintainers replace implementation work once supplied by external contributors? For human-centric software engineering, keeping humans in control of AI agents is not enough: AI can replace implementation labor while weakening how OSS communities renew themselves.
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· 1 citation
This research note examines distinctions and evaluation questions for AI-assisted software production through a hypothetical enrollment failure and a selective reading of the literature, and considers how quality engineering can evaluate both generated implementations and the processes that produce them.
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.
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A theoretical framework is constructed that expands assumptions about individual motivation to include the idea of a long-term, value-informed quest beyond short-term rewards, and contains three theoretical conjectures that seek to explain how collectively elaborated standards of excellence prompt developers to produce...
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An exploratory review of the emerging gray literature, which largely agrees on what a well-engineered loop contains: triggered agent runs bounded by machine-checkable stop conditions, persistent state files, verifier sub-agents, token budgets, and defined points of escalation to humans.
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