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LLM-based System Design Automation (LSDA) Using Generative AI: Exploring a Formalized Framework Based on Experiments

Year: 2025

Type: article

Abstract: Since the 1968 NATO Conference on Software Engineering laid the groundwork for systematic development of software systems, much effort has been dedicated to automating software design including Computer-Aided Software Engineering, Model-Driven Development, low-code/no-code platforms, and AI-driven tools like ChatGPT from OpenAI. Today, Generative AI offers an unprecedented opportunity for converting user requirements in natural language into system specifications via a Large Language Model (LLM). In this paper, we validate LLM-based system design automation (LSDA) for modeling ontology, workflow, entity-relationship diagrams and other artifacts on real-world examples. We found that ChatGPT is able to produce these artifacts with surprisingly high accuracy and quality even with a zero-shot prompting approach and simple prompts with little prompt optimization.

Source: Journal of the Association for Information Systems

Authors: Kumar, Akhil, Zhao, J. Leon

Institutions: Pennsylvania State University, Chinese University of Hong Kong

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7067247835

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