# LLM-based Automated Architecture View Generation: Where Are We Now?

**Authors:** Miryala Sathvika, Rudra Dhar, Karthik Vaidhyanathan  
**arXiv:** [2603.21178](https://arxiv.org/abs/2603.21178)  
**Date:** 2026-03-22  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2603.21178v1/

## Key points

- Creation of architecture views is crucial for software architecture documentation
- Manual process can be labor-intensive and often results in outdated artifacts
- Automated generation of views from source code becomes increasingly valuable as systems become more complex
- Empirical evaluation of Language Model (LLMs) and agentic approaches in generating architecture views from source code
- Generated 4,137 high-quality architecture views from 340 open-source repositories using three LLMs with three prompting techniques and two agentic approaches
- Prompting strategies offered marginal improvements while a custom agentic approach consistently outperformed a general-purpose agent
- LLMs and agentic approaches exhibited granularity mismatches by operating at the code level rather than architectural abstractions
- Human expertise in architectural design processes is still needed, positioning LLMs and agentic approaches as assistive tools rather than autonomous architects
- Challenges encountered informed experimental design and implemented solutions such as retry mechanism for incorrect PlantUML code and hierarchical summarization approach to manage context window constraints
- Addressing source code summarization is critical bottleneck in accurately generating high-level architectural views from source code

## Summary

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The creation of architecture views is crucial for software architecture documentation, but the manual process can be labor-intensive and often results in outdated artifacts. As systems become more complex, the automated generation of views from source code becomes increasingly valuable. In this study, we aimed to empirically evaluate the effectiveness of Language Model (LLMs) and agentic approaches in generating architecture views from source code. Our comprehensive analysis of 340 open-source repositories across 13 experimental configurations using three LLMs with three prompting techniques and two agentic approaches resulted in the generation of 4,137 high-quality architecture views. We assessed their quality through a combination of automated metrics and human evaluations, revealing that prompting strategies offered marginal improvements while a custom agentic approach consistently outperformed a general-purpose agent. However, despite their capabilities, LLMs and agentic approaches exhibited granularity mismatches by operating at the code level rather than architectural abstractions. This highlights the continued need for human expertise in architectural design processes, positioning them as assistive tools rather than autonomous architects. Throughout our study, we encountered various challenges that informed our experimental design and implemented solutions such as a retry mechanism for incorrect PlantUML code and a hierarchical summarization approach to manage context window constraints. Overall, our research underscores the importance of addressing source code summarization as a critical bottleneck in accurately generating high-level architectural views from source code.