Structure- and Event-Driven Frameworks for State Machine Modeling with Large Language Models

Abstract

UML state machine design is a critical process in software engineering. Traditionally, state machines are manually crafted by experienced engineers based on natural language requirements-a time-consuming and error-prone procedure. Many automated approaches exist but they require structured NL requirements. In this paper, we investigate the capabilities of current Large Language Models to fully automate UML state machine generation via specialized State Machine Frameworks (SMFs) from non-structured NL requirements. We evaluate two types of state-of-the-art LLMs using single-step and multi-step prompting approaches: a non-reasoning LLM GPT-4o and a reasoning-focused LLM Claude 3.5 Sonnet, and introduce a novel Hybrid Approach that uses the output from a Single-Prompt Baseline as an initial draft state machine, which is then refined through an SMF. In our study, two distinct SMFs are developed based on human approaches: (i) a Structure-Driven SMF, in which state machine components (states, transitions, guards, actions, etc.) are generated in sequential steps, and (ii) an Event-Driven SMF, where identified events iteratively guide state machine construction. Our experiments indicate that while LLMs demonstrate a promising ability to generate state machine models from the Single-Prompt Baseline (e.g., F1-scores of 0.90 for states and 0.75 for transitions using Claude 3.5 Sonnet), their performance is not yet fully sufficient for a fully automated solution (e.g., F1-scores of 0.23 for guards and 0.00 for actions for GPT-4o). Our proposed Hybrid Approach improves the performance of the non-reasoning LLM (GPT-4o) to a similar level as the reasoning LLM (Claude 3.5 Sonnet) but does not further improve the reasoning LLM. Our evaluation highlights both the potential and the limitations of current LLMs for automated state machine design, providing a baseline for future research in this domain.