mlsanityData to 7 Oct 2026

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

arXiv 2602.18644 ↗First posted 20 Feb 2026cs.SEcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 24 references that are in the map. First posted on arXiv 20 Feb 2026.

Builds on

3 of 24 references.

References · 3

  1. 2023MeROS: SysML-based Metamodel for ROS-based Systemscited by 6
  2. 2025Generating Software Architecture Description from Source Code using Reverse Engineering and Large Language Modelcited by 4
  3. 2022Towards Automatically Extracting UML Class Diagrams from Natural Language Specificationscited by 3

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Citing papers per year: 2026: 2.

2 papers.

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