<![CDATA[Safety-critical software systems in the automotive, aerospace, and industrial sectors have grown to millions of lines of source code, making traditional approaches to software documentation, architecture comprehension, and knowledge transfer increasingly impractical. As continuous integration and rapid release cycles accelerate software evolution, organizations face persistent challenges related to technical drift, loss of domain expertise, fragmented architectural knowledge, and the dependence on a small number of senior developers. Conventional code discovery techniques based on lexical or keyword searches provide limited support because they identify only literal text rather than the underlying functional semantics of software artifacts. This paper proposes the Dynamic Code Wiki, an intelligent software knowledge architecture that automatically transforms large codebases into continuously evolving, semantically searchable knowledge repositories. The proposed framework integrates Abstract Syntax Tree (AST) parsing, vector-based semantic embeddings, retrieval-augmented generation (RAG), and automated knowledge graph construction to generate structured documentation directly from source code and development artifacts. The architecture further strengthens compliance with functional safety standards by automatically linking software requirements to implementation components and performing call-graph analysis to identify subsystems affected by code modifications. In addition, engineering knowledge embedded within commit messages, code review discussions, defect reports, and software evolution history is preserved as a permanent, searchable organizational knowledge graph independent of individual developers. By combining semantic code understanding with automated documentation and traceability, the proposed framework significantly improves software maintainability, architectural transparency, impact analysis, and long-term knowledge preservation. The Dynamic Code Wiki provides a scalable engineering solution for modern safety-critical software development, enabling organizations to reduce knowledge loss, accelerate developer onboarding, strengthen regulatory compliance, and support continuous software evolution without relying solely on manually maintained documentation or the institutional memory of experienced engineers]]>
