From Scattered to Structured: A Vision for Automating Architectural Knowledge Management
Jan Keim, Angelika Kaplan
TL;DR
The paper addresses the problem of dispersed architectural knowledge and erosion across evolving software systems. It proposes an automated pipeline that extracts knowledge from heterogeneous artifacts, links and reconciles it, and consolidates it into a structured knowledge base to support conformance checking, change impact analysis, and natural-language access, aided by an agent for continuous monitoring. Core contributions include a plan for specialized extractors, a unified KB schema with traceability, automated consistency resolution, agent-based artifact monitoring, and a retrieval-augmented QA interface. The approach promises to democratize access to architectural knowledge, improve maintenance and evolution decisions, and reduce cognitive load on developers by surfacing trustworthy, up-to-date information.
Abstract
Software architecture is inherently knowledge-centric. The architectural knowledge is distributed across heterogeneous software artifacts such as requirements documents, design diagrams, code, and documentation, making it difficult for developers to access and utilize this knowledge effectively. Moreover, as systems evolve, inconsistencies frequently emerge between these artifacts, leading to architectural erosion and impeding maintenance activities. We envision an automated pipeline that systematically extracts architectural knowledge from diverse artifacts, links them, identifies and resolves inconsistencies, and consolidates this knowledge into a structured knowledge base. This knowledge base enables critical activities such as architecture conformance checking and change impact analysis, while supporting natural language question-answering to improve access to architectural knowledge. To realize this vision, we plan to develop specialized extractors for different artifact types, design a unified knowledge representation schema, implement consistency checking mechanisms, and integrate retrieval-augmented generation techniques for conversational knowledge access.