A Local Human‑in‑the‑Loop Assistant for AI‑Assisted SVN Commit‑Message Generation

This technical report presents svnₐiₘsg, an open‑source command‑line assistant for generating commit‑message candidates from Subversion working copies. The tool combines deterministic repository inspection, lightweight rule‑based processing, and local Large Language Model inference through Ollama. The proposed workflow extracts information from svn status and selected diff content, generates multiple commit‑message candidates, and preserves user control over the final commit operation. The implementation is designed for local execution in order to support source‑code confidentiality, reproducibility, and integration with lightweight command‑line environments. A preliminary functional evaluation on representative SVN changes indicates that the proposed workflow can generate useful commit‑message candidates while maintaining a human‑in‑the‑loop interaction model. The source code is publicly available and archived with a persistentDOI.