arXiv:2606.26289 · v2
Augmentation with Dilution? A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption
摘要
AI coding agents are rapidly proliferating in open-source software development, and the long-term sustainability of open-source projects is closely tied to the health of their human contributor ecosystems. Yet most existing studies treat human contributors as a static backdrop, and the available evidence on human participation after agent adoption mainly concerns newcomer outcomes. This paper takes the human contributor ecosystem as its dependent variable. To the best of our knowledge, it is the first study to estimate how agent adoption changes the number of distinct humans behind each pull request, and to relate this change to agent pull requests recorded under developer accounts. Using the AIDev dataset, GitHub API scanning, agent-trace screening, and two-stage propensity score matching, we construct a balanced panel of 2,246 treated and 3,798 control repositories from January 2023 to May 2026, and estimate effects with the Sun-Abraham estimator for staggered difference-in-differences, event studies, and HonestDiD sensitivity analysis. AI agent adoption is associated with a decline in human contributor density of 0.019 to 0.048, about 4-10% of its pre-adoption level, depending on the reference month (both p < 0.01); because this estimate tolerates only small violations of parallel trends, we regard it as suggestive evidence. The share of newcomers shows no detectable change (ATT = -0.009, p = 0.28), with the 95% confidence interval ruling out a decrease of more than 2.4 percentage points. Whether the decline in density is associated with agent pull requests recorded under developer accounts remains inconclusive (high minus low share: -0.013, 95% CI [-0.083, 0.057]). These findings are consistent with a pattern of augmentation with dilution: we detect no decrease in the share of newcomers, but each human contributor appears to correspond to more pull requests.
作者备注：v2: Substantially revised. Outcome variables are now computed from complete pull request histories retrieved through the GitHub API instead of GHArchive, whose coverage of pull request events declines from mid-2025. The decrease in the newcomer share reported in v1 is not supported in this version, and the review-depth analysis has been removed