A Text Mining and Classification Approach for Analyzing Architecture Decision Records

• URL: http://arxiv.org/abs/2609.07375v1
• Date: Mon, 07 Sep 2026 11:52:20 GMT
• Title: A Text Mining and Classification Approach for Analyzing Architecture Decision Records
• Abstract summary: We propose an automated text-mining and classification approach for analyzing architectural decision records at scale.<n>Our analysis examines decision and quality attributes, and the degree to which ADRs adhere to the MADR template.<n>Our findings show that ADRs frequently capture existence, technology, and process-related decisions, while alternatives, decisions drivers, and some quality concerns remain under-documented.
• Score: 0.45880283710344066
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Architectural decision records (ADRs) have become a popular lightweight mechanism for documenting architectural knowledge in software projects. However, there is limited empirical evidence on the kinds of architectural concerns captured in ADRs and how well their contents align with established architectural knowledge concepts and documentation practices. In this paper, we propose an automated text-mining and classification approach for analyzing ADRs at scale. We apply this approach to a dataset of ADRs extracted from ~550 open-source repositories, combining topic modeling, LLM-based classification, and template compliance checks. Our analysis examines decision taxonomies and quality attributes, and the degree to which ADRs adhere to the MADR template. Our findings show that ADRs frequently capture existence, technology, and process-related decisions, while alternatives, decisions drivers, and some quality concerns remain under-documented. We also observe recurring mismatches between ADR contents and template sections. These insights into current documentation practices provide architects with valuable information to reflect on how ADRs are and should be used to effectively deal with architectural knowledge. Furthermore, our automated approach is adaptable to other architectural tasks.

Related papers

• Towards Knowledgeable Deep Research: Framework and Benchmark [105.87257445617922]
  This paper introduces Knowledgeable Deep Research (KDR), which requires DR agents to generate reports with both structured and unstructured knowledge.<n>We propose the Hybrid Knowledge Analysis framework (HKA), a multi-agent architecture that integrates texts, figures, and tables into coherent multimodal reports.<n> Experimental results demonstrate that HKA consistently outperforms most existing DR agents on general-purpose and knowledge-centric metrics.
  arXiv  Detail & Related papers  (2026-04-09T02:06:27Z)
• AgenticAKM : Enroute to Agentic Architecture Knowledge Management [2.625250413679978]
  Architecture Knowledge Management (AKM) is crucial for maintaining current and comprehensive software Architecture Knowledge (AK) in a software project.<n>We propose an Agentic approach for AKM, AgenticAKM, where the complex problem of architecture recovery and documentation is decomposed into manageable sub-tasks.
  arXiv  Detail & Related papers  (2026-02-04T11:16:07Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness [15.810758425275322]
  We propose Retrieve-DocumentRoute-Read (RDR2), a novel framework that explicitly incorporates structural information throughout the RAG process.<n>RDR2 employs an LLM-based router to dynamically navigate document structure trees, jointly evaluating content relevance and hierarchical relationships to assemble optimal evidence.<n>Our key innovation lies in formulating document routing as a trainable task, with automatic action curation and structure-aware passage selection inspired by human reading strategies.
  arXiv  Detail & Related papers  (2025-10-05T17:04:24Z)
• DREAM: Document Reconstruction via End-to-end Autoregressive Model [53.51754520966657]
  We present an innovative autoregressive model specifically designed for document reconstruction, referred to as Document Reconstruction via End-to-end Autoregressive Model (DREAM)<n>We establish a standardized definition of the document reconstruction task, and introduce a novel Document Similarity Metric (DSM) and DocRec1K dataset for assessing the performance of the task.
  arXiv  Detail & Related papers  (2025-07-08T09:24:07Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• ATRAF-driven IMRaD Methodology: Tradeoff and Risk Analysis of Software Architectures Across Abstraction Levels [0.0]
  evaluating architectural artifacts is essential to assess tradeoffs and risks affecting quality attributes such as performance, modifiability, and security.<n>Our prior work introduced the Architecture Tradeoff and Risk Analysis Framework (ATRAF)<n>This paper presents the ATRAF-driven IMRaD methodology, a concise method to align ATRAF's phases with IMRaD sections.
  arXiv  Detail & Related papers  (2025-05-06T15:22:28Z)
• The Architecture Tradeoff and Risk Analysis Framework (ATRAF): A Unified Approach for Evaluating Software Architectures, Reference Architectures, and Architectural Frameworks [0.0]
  We introduce the Architecture Tradeoff and Risk Analysis Framework (ATRAF)<n>ATRAF is a scenario-driven framework for evaluating tradeoffs and risks across architectural levels.<n>It enables the identification of sensitivities, tradeoffs, and risks while supporting continuous refinement of architectural artifacts.
  arXiv  Detail & Related papers  (2025-05-01T17:48:52Z)
• 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)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [115.72130322143275]
  REAR is a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA) We develop a novel architecture for LLM-based RAG systems, by incorporating a specially designed assessment module. Experiments on four open-domain QA tasks show that REAR significantly outperforms previous a number of competitive RAG approaches.
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures [2.4150871564195007]
  We propose a method to generate software architecture candidates based on requirements using artificial intelligence techniques. We further envision an automatic evaluation and trade-off analysis of the generated architecture candidates.
  arXiv  Detail & Related papers  (2024-01-25T10:56:58Z)

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.