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AI Research
arXiv CS.AI·May 21·5 min read
Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction
TL;DR
Complex robotic systems using ROS~2 often lack clear software architecture models, making it hard to understand their structure. A multi-level agent-based approach was developed to assist in recovering hierarchical architecture from distributed artifacts.
✦ Why It Matters
Engineers can leverage this approach to improve architecture recovery in ROS~2 systems, enhancing system maintenance and evolution.
Key Takeaways
- Utilize agent-based methods for architecture recovery in ROS~2 systems.
- Focus on distributed artifacts for clearer system understanding.
- Implement multi-level analysis to enhance architecture visibility.
- Adopt this framework for better system maintenance and evolution.
Full Summary
Software architecture models are crucial for understanding and evolving complex systems, yet in ROS~2 (Robot Operating System 2) environments, these models are often implicit, scattered across various components like source code and configuration files. To address this, a novel multi-level agent-based approach was created to assist in recovering the hierarchical structure of these systems.
This method employs agents that analyze and reconstruct architecture from distributed artifacts, enhancing the visibility of system components and their interactions. The approach was tested on real-world ROS~2 systems, demonstrating significant improvements in architecture recovery accuracy.
Results showed a marked increase in the ability to identify and represent system structures, which is vital for effective system maintenance and evolution. This work has implications for software engineers, as it provides a framework for better understanding and managing complex robotic systems.