{"slug": "mining-architectural-quality-under-agentic-ai-adoption", "title": "Mining Architectural Quality Under Agentic AI Adoption", "summary": "A staggered difference-in-differences study of 151 open-source Java repositories, 74 with detectable agentic AI adoption and 77 propensity-matched controls, found that agentic AI coding tools left total architectural smell counts essentially unchanged (+1.1%, p = 0.82) while lines of code grew 12.8% (p = 0.003), producing a 6.7% decline in architectural smell density (p = 0.004) that the authors attribute to a denominator effect rather than architectural improvement. The study, submitted to arXiv on 11 June 2026, drew on 1,811 monthly Arcan snapshots across a 13-month per-repository window and used the Borusyak imputation estimator, with wild cluster bootstrap, Lee bounds and stale-observation sensitivity checks corroborating the pattern and flat pre-trends (Wald p = 0.90). The authors conclude that density-normalized outcomes can mislead when treatment affects system size, and that raw counts plus explicit decomposition are required for causal mining studies of AI tool adoption; the replication package and curated 151-repository monthly panel are publicly available.", "body_md": "# Computer Science > Software Engineering\n\n  [Submitted on 11 Jun 2026]\n\n# Title:Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories\n\n[View PDF](https://arxiv.org/pdf/2606.13298)\n\n[HTML (experimental)](https://arxiv.org/html/2606.13298v1)\n\nAbstract: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 effect of adoption on architectural smell density (ASD) with a staggered difference-in-differences design and the Borusyak imputation estimator, applying a causal design recently used for code-level metrics to the architecture level. Total smell counts are essentially unchanged (+1.1%, p = 0.82) while lines of code grow +12.8% (p = 0.003); the resulting 6.7% ASD decline (p = 0.004) is therefore a denominator effect rather than an architectural improvement. Per-type estimates and robustness checks (wild cluster bootstrap, Lee bounds, stale-observation sensitivity) corroborate the pattern; pre-trends are flat (Wald p = 0.90), consistent with parallel trends. Density-normalized outcomes can mislead when treatment affects system size: raw counts and explicit decomposition are required for causal mining studies of AI tool adoption. The complete replication package, including the curated 151-repository monthly panel, is publicly available.\n    \n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/mining-architectural-quality-under-agentic-ai-adoption", "canonical_source": "https://arxiv.org/abs/2606.13298", "published_at": "2026-10-21 14:57:02+00:00", "updated_at": "2026-10-21 15:13:46.203988+00:00", "lang": "en", "topics": ["ai-research", "artificial-intelligence", "ai-tools", "machine-learning"], "entities": ["arXiv", "Java", "Arcan", "Borusyak"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/mining-architectural-quality-under-agentic-ai-adoption", "markdown": "https://wpnews.pro/news/mining-architectural-quality-under-agentic-ai-adoption.md", "text": "https://wpnews.pro/news/mining-architectural-quality-under-agentic-ai-adoption.txt", "jsonld": "https://wpnews.pro/news/mining-architectural-quality-under-agentic-ai-adoption.jsonld"}}