Architectural Degradation: How to Measure and to Remediate

2026-10-01

AI Summary

This study addresses the fragmentation in software architecture degradation research by systematically elucidating the intrinsic relationships among measurements, metrics, and remediation strategies. It proposes an automated literature analysis pipeline leveraging local large language models and retrieval-augmented generation to construct a structured taxonomy of diagnosis and repair approaches through multi-source reviews. This work identifies hundreds of measurement methods and tools, revealing a critical gap in the field: while diagnostic capabilities are mature, remediation efforts remain insufficiently integrated. Ultimately, it provides a unified framework for the systematic investigation of architectural degradation and outlines future research directions.

Abstract

Context. Architectural degradation undermines software maintainability, evolvability, and quality. However, existing research remains fragmented across measurement approaches, metrics, tools, and remediation strategies, limiting our understanding of how these elements relate across the degradation lifecycle. Aim. We consolidate the state of the art on architectural degradation by examining how researchers measure it, which metrics and tools support its assessment, and how existing approaches address remediation. Method. We conducted a Multivocal Literature Review of 284 peer-reviewed and grey-literature studies. We supported screening, data extraction, and classification with a locally executed LLM-assisted pipeline combining Retrieval-Augmented Generation, multi-model validation, and human adjudication. We then analyzed the resulting taxonomies and their cross-dimensional relationships. Results and Conclusions. We identified 277 measurement approaches, 357 metrics, 238 tools, and 395 remediation approaches. Research strongly concentrates on static and structural analysis, structural metrics, and detection-oriented tools. In contrast, remediation spans heterogeneous code-level, architectural, and organizational interventions and shows substantially less consolidation. Overall, the field has developed a mature diagnostic apparatus but has made less progress in connecting degradation detection with effective remediation. Our results provide a structured view of the available techniques and identify the diagnosis-remediation gap as a key direction for future research.

Problem

• Architectural Degradation
• Measurement
• Remediation
• Software Maintainability
• Diagnosis-Remediation Gap

Innovation

• Architectural Degradation
• Retrieval-Augmented Generation
• LLM-assisted Pipeline
• Multivocal Literature Review
• Diagnosis-Remediation Gap