LLM-Based Approach for Enhancing Maintainability of Automotive Architectures

• URL: http://arxiv.org/abs/2509.12798v1
• Date: Tue, 16 Sep 2025 08:17:41 GMT
• Title: LLM-Based Approach for Enhancing Maintainability of Automotive Architectures
• Authors: Nenad Petrovic, Lukasz Mazur, Alois Knoll,
• Abstract summary: In this paper, we explore the potential of Large Language Models (LLMs) when it comes to the automation of tasks and processes that aim to increase the flexibility of automotive systems.<n>Three case studies are considered as outcomes of early-stage research: 1) updates, hardware abstraction, and compliance, 2) interface compatibility checking, and 3) architecture modification suggestions.<n>For proof-of-concept implementation, we rely on OpenAI's GPT-4o model.
• Score: 29.585470064353014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are many bottlenecks that decrease the flexibility of automotive systems, making their long-term maintenance, as well as updates and extensions in later lifecycle phases increasingly difficult, mainly due to long re-engineering, standardization, and compliance procedures, as well as heterogeneity and numerosity of devices and underlying software components involved. In this paper, we explore the potential of Large Language Models (LLMs) when it comes to the automation of tasks and processes that aim to increase the flexibility of automotive systems. Three case studies towards achieving this goal are considered as outcomes of early-stage research: 1) updates, hardware abstraction, and compliance, 2) interface compatibility checking, and 3) architecture modification suggestions. For proof-of-concept implementation, we rely on OpenAI's GPT-4o model.

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