The arXiv paper “Mining Architectural Quality Under Agentic AI Adoption” (arXiv:2606.13298, Oliver Larsen et al.) reports a causal study of open-source Java repositories. Per the arXiv abstract, the authors mine 151 repositories, 74 with detectable agentic AI adoption and 77 propensity-matched controls, across a 13-month per-repository window producing 1,811 monthly Arcan snapshots. Using a staggered difference-in-differences design and the Borusyak imputation estimator, they estimate effects on architectural smell density (ASD). The paper reports total smell counts are essentially unchanged (+1.1%, p = 0.82), lines of code grow +12.8% (p = 0.003), and ASD declines 6.7% (p = 0.004); the authors characterize the ASD decline as a denominator effect rather than an architectural improvement. The study reports flat pre-trends and multiple robustness checks and publishes a complete replication package, per arXiv.
