Data Set for 15 Microservices Based Application: Before and After Applying Microservices Design Pattern

Microservices architecture offers theoretical benefits for software maintainability, yet empirical validation of design patterns' effectiveness for enhancing modifiability remains limited. This study presents 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 applied using a context-sensitive selection framework derived from ISO 25002:2024 standards. The framework encompasses Change Impact Factor (CIF), Service Independence Metric (SIM), and Modifiability Index (MI). Results 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). Compared to previous application-level studies showing higher improvement rates (CIF: -53.4%, SIM: +48.4%), this service-level analysis reveals more conservative but statistically robust improvements, reflecting the granular nature of individual service optimization. API Gateway demonstrated superior overall effectiveness (MI improvement: +0.496), while Anti-Corruption Layer specialized in change impact reduction. Strong correlations between initial architectural characteristics and improvement magnitude revealed the \u201cWorst First\u201d principle, where services with poorest metrics benefit most from pattern application. The resulting evidence-based framework enables context-sensitive pattern selection, moving beyond universal approaches to design pattern application in microservices architectures