Governed vs Autonomous AI Coding: Structural Drift, Architectural Entropy, and Empirical Evidence from Three Longitudinal Case Studies

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Authors: Espen Sande-Larsen
Cited by: 0

Abstract

This companion study presents longitudinal empirical analysis of three open-source repositories using the EntropyX structural entropy framework. The results indicate that fully autonomous AI coding phases correlate with rapid entropy accumulation, whereas human-governed hybrid approaches demonstrate bounded structural evolution and measurable entropy cooling. The study introduces anomaly detection and entropy slope modeling for architectural governance. The work is reproducible using open tooling and public repositories.

Fields of Study & Subjects

Software Engineering Research Software Engineering Techniques and Practices Advanced Software Engineering Methodologies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
27 February 2026
Language
EN