ModARO: A Modular Approach to Architecture Reconstruction of Distributed Microservice Codebases

2026-02-09

AI Summary

This work addresses the challenges posed by microservice architectures—such as missing documentation, difficulty in understanding system structure, and architectural drift—stemming from decentralized, multi-repository development practices. Existing refactoring approaches struggle to generalize across projects due to their lack of modularity and technology-stack dependency. To overcome these limitations, we propose ModARO, a modular and technology-agnostic architecture reconstruction method that leverages reusable “extractors” to automatically analyze distributed codebases and reconstruct system architectures across projects. ModARO seamlessly integrates into CI/CD pipelines, enabling continuous architectural awareness. Empirical evaluation demonstrates its successful application to ten open-source systems, and a user study with eight industry practitioners confirms its significant superiority over existing baselines in terms of practicality and usability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Microservice architectures promote small, independently developed services, but increase overall architectural complexity. It is crucial that developers understand the architecture and how changes to a service affect the overall system, but rapid and independent development of services increases the risk of architectural drift and discourages the creation and maintenance of documentation. Automatic architecture reconstruction can help avoid these issues, but it is difficult to reuse reconstruction code across multiple projects, as all use different combinations of technologies and project-specific conventions. Reconstruction of architecture-level details is further complicated by the tendency to split microservices into separate repositories, preventing a full view of the system from any one codebase. In this paper, we present and evaluate ModARO, an approach to microservice architecture reconstruction that allows writing modular reconstruction code ('extractors') for any technologies and reusing them across different projects, independent of the surrounding technology stack or whether or not the services are split into multiple codebases. We demonstrate the effectiveness of our approach by configuring ModARO to reconstruct 10 open source projects, and we validate the usefulness and usability of ModARO against a state-of-the-art baseline in a user study with 8 industry practitioners. Using this approach, developers can assemble or create extractors tailored to their technology stacks and distribute architecture reconstruction across repositories, enabling integration into repository CI/CD pipelines.

Problem

• microservice architecture
• architectural drift
• architecture reconstruction
• modular reconstruction
• multi-repository systems

Innovation

• Modular Architecture Reconstruction
• Microservice Architecture
• Extractor Reusability
• Cross-Repository Analysis
• CI/CD Integration