Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 26·5 min read
Code2UML: Agentic LLMs with context engineering for scalable software visualization
TL;DR
Existing Large Language Model (LLM)-based tools struggle with scalability when analyzing large codebases for documentation. Code2UML is introduced, utilizing a hierarchy of five specialized agents to generate UML diagrams from source code efficiently.
✦ Why It Matters
Engineers can leverage Code2UML for efficient UML diagram generation from large codebases without LLM limitations.
Key Takeaways
- Utilize Code2UML for efficient UML diagram generation.
- Leverage specialized agents for distinct cognitive tasks.
- Implement importance-weighted IR compaction for scalability.
- Achieve high accuracy across multiple programming languages.
How It Works
Code2UML employs a hierarchy of five specialized agents, each responsible for a specific cognitive subtask in UML diagram generation. The importance-weighted IR compaction layer condenses extensive project data into focused views that fit within LLM token limits, allowing for rapid processing without direct LLM calls.