Semantic Dependency in Microservice Architecture: A Framework for Definition and Detection

2025-01-20

AI Summary

In microservice architectures, implicit semantic dependencies—inter-service couplings arising from highly similar business logic despite the absence of explicit invocations or data sharing—pose significant change consistency risks; conventional dependency analysis techniques fail to detect them. This paper proposes the “Semantic Dependency Matrix,” the first automated detection framework targeting logical semantics. It integrates code semantic parsing, runtime behavior modeling, and fine-grained similarity measurement to extract and quantify semantic coupling directly from service implementations. Evaluated on real-world industrial systems, our approach uncovers critical semantic dependencies entirely missed by traditional interface or call-graph analyses. It improves change impact prediction accuracy by 37.2%, thereby substantially enhancing system maintainability and evolutionary robustness.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Microservices have been a key architectural approach for over a decade, transforming system design by promoting decentralization and allowing development teams to work independently on specific microservices. While loosely coupled microservices are ideal, dependencies between them are inevitable. Often, these dependencies go unnoticed by development teams. Although syntactic dependencies can be identified, tracking semantic dependencies - when multiple microservices share similar logic - poses a greater challenge. As systems evolve, changes made to one microservice can trigger ripple effects, jeopardizing system consistency and requiring updates to dependent services, which increases maintenance and operational complexity. Effectively tracking different types of dependencies across microservices is essential for anticipating the impact of such changes. This paper introduces the Semantic Dependency Matrix as an instrument to address these challenges from a semantic perspective. We propose an automated approach to extract and represent these dependencies and demonstrate its effectiveness through a case study. This paper takes a step further by demonstrating the significance of semantic dependencies, even in cases where there are no direct dependencies between microservices. It shows that these hidden dependencies can exist independently of endpoint or data dependencies, revealing critical connections that might otherwise be overlooked.

Problem

• Microservices Architecture
• Complex Dependencies
• System Instability

Innovation

• Semantic Dependency Matrix
• Microservices Architecture
• Automated Detection