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

• URL: http://arxiv.org/abs/2602.18644v1
• Date: Fri, 20 Feb 2026 22:36:47 GMT
• Title: Modeling and Recovering Hierarchical Structural Architectures of ROS 2 Systems from Code and Launch Configurations using LLM-based Agents
• Authors: Mohamed Benchat, Dominique Briechle, Raj Chanchad, Mitbhai Chauhan, Meet Chavda, Ruidi He, Dhruv Jajadiya, Dhruv Kapadiya, Nidhiben Kaswala, Daniel Osterholz, Andreas Rausch, Meng Zhang,
• Abstract summary: subsystem-level recall drops with repository complexity due to implicit launch semantics.<n>We evaluate the approach on three ROS2 repositories, including an industrial-scale code subset.
• Score: 3.4923417716883
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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