# Assessing LLMs for Front-end Software Architecture Knowledge

arXiv: https://arxiv.org/abs/2502.19518 · HTML: https://mlsanity.com/paper/2502.19518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Requirements are All You Need: From Requirements to Code with LLMs](https://mlsanity.com/paper/2406.10101/) (2024)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [From Image to UML: First Results of Image Based UML Diagram Generation Using LLMs](https://mlsanity.com/paper/2404.11376/) (2024)
- [SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model](https://mlsanity.com/paper/2401.13266/) (2024)
- [How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts](https://mlsanity.com/paper/2404.17739/) (2024)
- [Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations](https://mlsanity.com/paper/2401.10364/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [(How) Do Large Language Models Understand High-Level Message Sequence Charts?](https://mlsanity.com/paper/2605.13773/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated significant promise in automating software development tasks, yet their capabilities with respect to software design tasks remains largely unclear. This study investigates the capabilities of an LLM in understanding, reproducing, and generating structures within the complex VIPER architecture, a design pattern for iOS applications. We leverage Bloom's taxonomy to develop a comprehensive evaluation framework to assess the LLM's performance across different cognitive domains such as remembering, understanding, applying, analyzing, evaluating, and creating. Experimental results, using ChatGPT 4 Turbo 2024-04-09, reveal that the LLM excelled in higher-order tasks like evaluating and creating, but faced challenges with lower-order tasks requiring precise retrieval of architectural details. These findings highlight both the potential of LLMs to reduce development costs and the barriers to their effective application in real-world software design scenarios. This study proposes a benchmark format for assessing LLM capabilities in software architecture, aiming to contribute toward more robust and accessible AI-driven development tools.