Automated Statechart Generation from Natural Language Requirements Using AI Techniques in Automotive Software Engineering

Abstract

Context: Statecharts are widely recognized as an effective modeling technique for describing system behaviors, allowing engineers to clearly represent states and transitions. However, the process of creating statecharts from textual requirements remains largely manual, requiring engineers’ expertise to interpret textual descriptions, define states and transitions, and identify potential errors. As systems grow increasingly complex, this approach faces challenges in scaling, leading to inefficiencies, inconsistencies, and delays in the design and validation phases.

Problem: Existing tools such as Stateflow, Yakindu, and Enterprise Architect, although useful for statechart modeling, still require significant manual effort. These tools are not designed to efficiently generate statecharts from unstructured textual input, which creates a challenge in maintaining the flexibility needed in rapidly evolving industrial settings. Despite advancements in large language models (LLMs), their potential for automating statechart generation has not been fully explored or used in real-world contexts, with a focus on the automotive industry.

Solution: This thesis proposes a framework that takes advantage of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Transformerbased models, to automatically generate statecharts from textual requirements. The approach integrates pre-trained language models with fine-tuned domain-specific data, enabling the identification of states, transitions, and the generation of valid statecharts directly from natural language input. This framework represents a significant step towards automating the statechart creation process, making it more efficient and reliable.

Results and Contribution: The results of this research show that fine-tuning LLMs on domain-specific data significantly improves the quality of the LLM generated statecharts in terms of functional correctness and understandability. By automating statechart generation, this work improves productivity, minimizes human error, and offers a scalable solution for the automotive industry. The proposed approach enhances design and validation workflows, enabling faster and more accurate system development in critical domains such as automotive engineering.