# LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World

arXiv: https://arxiv.org/abs/2502.02539 · HTML: https://mlsanity.com/paper/2502.02539/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2025.

## Built on by (most cited first, 8 of 8)

- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [Generating Energy-Efficient Code via Large-Language Models -- Where are we now?](https://mlsanity.com/paper/2509.10099/) (2025)
- [Can AI Agents Generate Microservices? How Far are We?](https://mlsanity.com/paper/2603.09004/) (2026)
- [Evaluating Large Language Models for Detecting Architectural Decision Violations](https://mlsanity.com/paper/2602.07609/) (2026)
- [LLM-based Automated Architecture View Generation: Where Are We Now?](https://mlsanity.com/paper/2603.21178/) (2026)
- [Toward a Sustainable Software Architecture Community: Evaluating ICSA's Environmental Impact](https://mlsanity.com/paper/2604.04096/) (2026)
- [Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey](https://mlsanity.com/paper/2605.01392/) (2026)
- [Architectural Constraints Alignment in AI-assisted, Platform-based Service Development](https://mlsanity.com/paper/2605.04973/) (2026)

## Abstract (arXiv)

Recently, the exponential growth in capability and pervasiveness of Large Language Models (LLMs) has led to significant work done in the field of code generation. However, this generation has been limited to code snippets. Going one step further, our desideratum is to automatically generate architectural components. This would not only speed up development time, but would also enable us to eventually completely skip the development phase, moving directly from design decisions to deployment. To this end, we conduct an exploratory study on the capability of LLMs to generate architectural components for Functions as a Service (FaaS), commonly known as serverless functions. The small size of their architectural components make this architectural style amenable for generation using current LLMs compared to other styles like monoliths and microservices. We perform the study by systematically selecting open source serverless repositories, masking a serverless function and utilizing state of the art LLMs provided with varying levels of context information about the overall system to generate the masked function. We evaluate correctness through existing tests present in the repositories and use metrics from the Software Engineering (SE) and Natural Language Processing (NLP) domains to evaluate code quality and the degree of similarity between human and LLM generated code respectively. Along with our findings, we also present a discussion on the path forward for using GenAI in architectural component generation.