Modeling and Recovering Hierarchical Structural Architectures of ROS 2 Systems from Code and Launch Configurations using LLM-based Agents

2026-02-20

AI Summary

This work addresses the challenge that ROS 2 systems often embed their layered architecture implicitly within launch configurations, lacking an explicit, standalone architectural view and thereby hindering maintainability. To overcome this, the paper introduces the first approach that models the layered structure as a first-class architectural view in ROS 2. It proposes an automated recovery method combining deterministic parsing with a large language model (LLM) agent, where UML modeling and structural contracts constrain the LLM’s synthesis process, and architectural blueprints guide verifiable, high-fidelity reconstruction. Evaluation on three ROS 2 repositories—including an industrial-scale subset—demonstrates high precision across all abstraction levels, though recall for subsystems declines in complex systems due to the implicit semantics of launch configurations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Model-Driven Engineering (MDE) relies on explicit architecture models to document and evolve systems across abstraction levels. For ROS~2, subsystem structure is often encoded implicitly in distributed configuration artifacts -- most notably launch files -- making hierarchical structural decomposition hard to capture and maintain. Existing ROS~2 modeling approaches cover node-level entities and wiring, but do not make hierarchical structural (de-)composition a first-class architectural view independent of launch artifacts. We contribute (1) a UML-based modeling concept for hierarchical structural architectures of ROS~2 systems and (2) a blueprint-guided automated recovery pipeline that reconstructs such models from code and configuration artifacts by combining deterministic extraction with LLM-based agents. The ROS~2 architectural blueprint (nodes, topics, interfaces, launch-induced wiring) is encoded as structural contracts to constrain synthesis and enable deterministic validation, improving reliability. We evaluate the approach on three ROS~2 repositories, including an industrial-scale code subset. Results show high precision across abstraction levels, while subsystem-level recall drops with repository complexity due to implicit launch semantics, making high-level recovery the remaining challenge.

Problem

• ROS 2
• hierarchical architecture
• structural decomposition
• launch configurations
• architecture recovery

Innovation

• Hierarchical Architecture
• ROS 2
• LLM-based Agents
• Model-Driven Engineering
• Automated Model Recovery