Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories
AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior causal work has measured code-level outcomes (complexity, static analysis warnings); whether such degradation propagates to architecture-level outcomes remains unknown. We mine 151 open-source Java repositories, 74 with detectable agentic AI adoption (identified via configuration files and Co-Authored-By commit trailers) and 77 propensity-matched controls, across a 13-month per-repository window yielding 1,811 monthly Arcan snapshots. We estimate the causal effe...
| Authors | Oliver Aleksander Larsen, Mahyar T. Moghaddam |
|---|---|
| Primary category | cs.SE |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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