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LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari +1
Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeli…
LLM-Empowered Functional Safety and Security by Design in Automotive Systems
Nenad Petrovic, Vahid Zolfaghari, Fengjunjie Pan +1
This paper presents LLM-empowered workflow to support Software Defined Vehicle (SDV) software development, covering the aspects of security-aware system topology design, as well as…
LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems
Nenad Petrovic, Norbert Kroth, Axel Torschmied +9
This paper presents an event-chain-driven, LLM-empowered workflow for generating validated, automotive code from natural-language requirements. A Retrieval-Augmented Generation (RA…
Hallucination in LLM-Based Code Generation: An Automotive Case Study
Marc Pavel, Nenad Petrovic, Lukasz Mazur +3
Large Language Models (LLMs) have shown significant potential in automating code generation tasks offering new opportunities across software engineering domains. However, their pra…
GenAI for Automotive Software Development: From Requirements to Wheels
Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari +3
This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabili…
Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
Nenad Petrovic, Vahid Zolfaghari, Andre Schamschurko +10
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for h…
Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulations
Krzysztof Lebioda, Nenad Petrovic, Fengjunjie Pan +3
Large Language Models (LLMs) are taking many industries by storm. They possess impressive reasoning capabilities and are capable of handling complex problems, as shown by their ste…
LLM-enabled Instance Model Generation
Fengjunjie Pan, Nenad Petrovic, Vahid Zolfaghari +2
In the domain of model-based engineering, models are essential components that enable system design and analysis. Traditionally, the creation of these models has been a manual proc…
LLM-based Iterative Approach to Metamodeling in Automotive
Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari +1
In this paper, we introduce an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM). The main focus is adoption in automotive domain.…
Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study
Nenad Petrovic, Yurui Zhang, Moaad Maaroufi +5
Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. Howeve…
Adopting RAG for LLM-Aided Future Vehicle Design
Vahid Zolfaghari, Nenad Petrovic, Fengjunjie Pan +2
In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the auto…
Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems
Nenad Petrovic, Fengjunjie Pan, Krzysztof Lebioda +6
We present a prototype of a tool leveraging the synergy of model driven engineering (MDE) and Large Language Models (LLM) for the purpose of software development process automation…
Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow
Krzysztof Lebioda, Viktor Vorobev, Nenad Petrovic +3
We propose a novel model- and feature-based approach to development of vehicle software systems, where the end architecture is not explicitly defined. Instead, it emerges from an i…