From Scattered to Structured: A Vision for Automating Architectural Knowledge Management

• URL: http://arxiv.org/abs/2601.19548v1
• Date: Tue, 27 Jan 2026 12:42:16 GMT
• Title: From Scattered to Structured: A Vision for Automating Architectural Knowledge Management
• Authors: Jan Keim, Angelika Kaplan,
• Abstract summary: We envision an automated pipeline that systematically extracts architectural knowledge from diverse artifacts.<n>This knowledge base enables critical activities such as architecture conformance checking and change impact analysis.
• Score: 0.9310318514564274
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• KnowCoder-V2: Deep Knowledge Analysis [64.63893361811968]
  We propose a textbfKnowledgeable textbfDeep textbfResearch (textbfKDR) framework that empowers deep research with deep knowledge analysis capability.<n>It introduces an independent knowledge organization phase to preprocess large-scale, domain-relevant data into systematic knowledge offline.<n>It then extends deep research with an additional kind of reasoning steps that perform complex knowledge computation in an online manner.
  arXiv  Detail & Related papers  (2025-06-07T18:01:25Z)
• Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead [8.68898878009242]
  This paper presents a forward-looking vision for artificial intelligence-driven software architecture.<n>We examine how artificial intelligence can automate architectural design, support quantitative trade-off analyses, and continuously update architectural documentation.<n>The resulting roadmap outlines 14 current artificial intelligence contributions to software architecture, identifies six artificial intelligence-specific challenges in supporting architectural tasks, and reveals six avenues for future improvement.
  arXiv  Detail & Related papers  (2025-04-06T03:00:07Z)
• A Survey on (M)LLM-Based GUI Agents [62.57899977018417]
  Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction.<n>Recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms.<n>This survey identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control.
  arXiv  Detail & Related papers  (2025-03-27T17:58:31Z)
• Semi-Automated Design of Data-Intensive Architectures [49.1574468325115]
  This paper introduces a development methodology for data-intensive architectures.<n>It guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application.<n>We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners.
  arXiv  Detail & Related papers  (2025-03-21T16:01:11Z)
• RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation [46.237206695937246]
  We propose Retrieval-And-Structuring (RAS), a framework that dynamically constructs query-specific knowledge graphs.<n>On seven knowledge-intensive benchmarks, RAS consistently outperforms strong baselines.<n>Our results demonstrate that dynamic, query-specific knowledge structuring offers a robust path to improving reasoning accuracy and robustness in language model generation.
  arXiv  Detail & Related papers  (2025-02-16T05:01:49Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems.<n>We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• Knowledge Management in the Companion Cognitive Architecture [7.136205674624813]
  We document some of the challenges we have faced in developing the knowledge stack for the Companion cognitive architecture. It is our hope that these observations will prove useful to other cognitive architecture developers facing similar challenges.
  arXiv  Detail & Related papers  (2024-07-08T21:20:05Z)
• Standardizing Knowledge Engineering Practices with a Reference Architecture [8.22187358555391]
  This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns.
  arXiv  Detail & Related papers  (2024-04-04T17:46:32Z)
• Architecture Knowledge Representation and Communication Industry Survey [0.0]
  We aim to understand the current practice in architecture knowledge, and to explore where sustainability can be applied to address sustainability in software architecture in the future. We used a survey, which utilized a questionnaire containing 34 questions and collected responses from 45 architects working at a prominent bank in the Netherlands.
  arXiv  Detail & Related papers  (2023-09-20T18:17:16Z)
• Enhancing Architecture Frameworks by Including Modern Stakeholders and their Views/Viewpoints [48.87872564630711]
  The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.<n>We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
  arXiv  Detail & Related papers  (2023-08-09T21:54:34Z)
• Identifying and Consolidating Knowledge Engineering Requirements [4.311189028205597]
  We propose to address four challenges by developing a reference architecture using a mainstream software methodology. By studying the requirements of different stakeholders and eras, we identify 23 essential quality attributes for evaluating reference architectures. We discuss the next steps towards a comprehensive reference architecture including prioritizing quality attributes, integrating components with complementary strengths, and supporting missing socio-technical requirements.
  arXiv  Detail & Related papers  (2023-06-27T00:26:15Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.