RAG-Based Semantic Retrieval for Software Update Triage
Abstract
Software update information is often fragmented across multiple sources such as vendor release notes, vulnerability disclosures, and community discussions. This fragmentation makes it difficult for maintainers and system administrators to identify and relate important updates and security issues in a timely manner. This paper presents a vendor-centered semantic retrieval framework that consolidates update information using similarity-based search within a Retrieval-Augmented Generation (RAG) architecture. The system integrates release notes, vulnerability records from the Common Vulnerabilities and Exposures (CVE) and National Vulnerability Database (NVD), and community feedback from Reddit discussions, embedding these artifacts into a shared vector space using sentence-transformer models and Facebook AI Similarity Search (FAISS) indexing. The framework was evaluated on a dataset of 1,200 software version updates, 248 Reddit discussions, and 312 vulnerability records collected over two years. Results show that semantic retrieval improves related update detection by 41 percent compared to keyword baselines, enables 36 percent earlier detection of critical security updates, and reduces manual inspection effort by about 30 percent. These findings indicate that vendor-centered semantic similarity can effectively connect fragmented software update information and support more timely update decisions.
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