Microservices Design Pattern in Action: Improving Modifiability in Microservices-Based Software Development | Zendy

Gintoro | Zendy; Ford Lumban Gaol | Zendy; Ahmad Nurul Fajar | Zendy; Abba Suganda Girsang | Zendy; Tokuro Matsuo | Zendy

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Microservices Design Pattern in Action: Improving Modifiability in Microservices-Based Software Development

Author(s) -

Gintoro,

Ford Lumban Gaol,

Ahmad Nurul Fajar,

Abba Suganda Girsang,

Tokuro Matsuo

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3610272

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Microservices architecture offers theoretical benefits for software maintainability, yet empirical validation of design patterns' effectiveness for enhancing modifiability remains limited, with practitioners lacking evidence-based guidance for pattern selection and quantified improvement expectations. This study addresses this gap by presenting a quantitative assessment of microservices design patterns' impact on modifiability through service-level analysis of 110 individual microservices across 15 open-source applications. Following Systematic Literature Review and Grey Literature analysis, ten modifiability-enhancing patterns were identified and evaluated using a novel context-sensitive selection framework derived from ISO 25002:2024 standards. The framework introduces three key theoretical contributions: the "Worst First" optimization principle for architectural refactoring prioritization, a service-level analysis methodology enabling granular pattern effectiveness assessment, and an evidence-based pattern selection model linking architectural characteristics to improvement potential. Quantitative evaluation using Change Impact Factor (CIF), Service Independence Metric (SIM), and Modifiability Index (MI) demonstrated significant improvements: CIF decreased by 49.7% (from 0.303 to 0.152), SIM increased by 14.2% (from 0.764 to 0.873), and MI improved by 17.6% (from 0.732 to 0.861). Statistical analysis confirmed significance for all metrics (CIF and MI: p<0.0001; SIM: p<0.05) with effect sizes ranging from small to large (d=0.26-0.81). Strong correlations between initial architectural characteristics and improvement magnitude (r=-0.812) validated the "Worst First" principle, where services with poorest metrics benefit most from pattern application. The resulting evidence-based framework enables context-sensitive pattern selection and provides quantified improvement expectations, moving beyond universal approaches to establish scientific foundations for architectural decision-making in microservices development.

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