Secure Coding Unleashed: Boosting Productivity With On-Premise LLM-Powered IDE Plugins

摘要

The integration of Large Language Model (LLM)-based code assistants into IDEs has boosted developer productivity, but cloud‑based solutions pose severe privacy risks for enterprises handling proprietary code. Existing on‑premise alternatives lack semantic code retrieval, multi‑GPU optimization, and rigorous productivity validation. We present an enterprise‑oriented, customizable IDE plugin that uses internally hosted LLMs, combining client‑side tiered retrieval (Personal CodeRAG with SiamBERT/CodeSage) and vLLM‑optimized inference on dedicated hardware. No code leaves the organization. Our benchmark of four inference frameworks shows vLLM and LMDeploy achieve $\approx$20 ms time‑to‑first‑token on LLaMA‑3‑8B, suitable for real‑time interaction. In a three‑month study with 100 developers, the plugin reduced mean cycle time by 15.4\%, with perceived productivity gains of 50-70\% for tasks like code explanation and unit test generation. This work provides the first systematic evaluation of on‑premise LLM‑assisted coding that quantifies both objective and subjective productivity impacts.