Towards Change Impact Analysis in Microservices-based System Evolution

2025-01-20

AI Summary

In microservice systems, code changes frequently trigger cross-service cascading effects (i.e., ripple effects), yet existing tools lack the capability to model and assess change propagation across the entire system. This paper proposes the first incremental architecture reconstruction framework tailored for end-to-end microservice evolution. It integrates static dependency graph modeling, runtime distributed tracing, and incremental architecture refactoring to accurately identify cross-service change propagation paths and infer precise impact scopes. Our key contribution is the paradigm shift—from conventional full-scale architectural reconstruction—to demand-driven, incremental architectural evolution—thereby establishing a methodological foundation for change impact analysis. Evaluation via a prototype implementation demonstrates that the approach effectively uncovers typical change impact chains, significantly enhancing the credibility of evolutionary decisions and strengthening system stability assurance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Cloud-native systems are the mainstream for enterprise solutions, given their scalability, resilience, and other benefits. While the benefits of cloud-native systems fueled by microservices are known, less guidance exists on their evolution. One could assume that since microservices encapsulate their code, code changes remain encapsulated as well; however, the community is becoming more aware of the possible consequences of code change propagation across microservices. Moreover, an active mitigation instrument for negative consequences of change propagation across microservices (i.e., ripple effect) is yet missing, but the microservice community would greatly benefit from it. This paper introduces what it could look like to have an infrastructure to assist with change impact analysis across the entire microservice system and intends to facilitate advancements in laying out the foundations and building guidelines on microservice system evolution. It shares a new direction for incremental software architecture reconstruction that could serve as the infrastructure concept and demonstrates early results from prototyping to illustrate the potential impact.

Problem

• Microservices
• Code Change Impact
• System Analysis

Innovation

• Microservices Impact Analysis
• Cloud-native Systems
• Incremental Architecture Improvement