Security Report Generation via LLM-RAG Assisted Directory Scanning: An Integrated Framework for Enhanced Software Documentation and Vulnerability Detection

Thorough and recent documentation is essential for software maintenance, security audits, and knowledge transfer. Yet most software projects lack complete or recent documentation. Current tools mostly produce low-level code summaries without incorporating external knowledge, resulting in inefficiencies and enhanced security risk. This paper introduces an LLM-RAG-augmented automated documentation system utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Information Retrieval (IR) methods. The system reads project directories, extracts metadata, and creates preliminary documentation using LLMs. A RAG module enhances this documentation by pulling external information pertinent to the task, like security advisories and bug reports, to provide a complete and actionable documentation framework. Evaluation is based on qualitative user studies and quantitative measurements. This work seeks to enhance documentation quality, increase software maintainability, and streamline security auditing through an AI-powered, explainable, and transparent report generation framework.