Vibe-driven model-based engineering

2026-04-12

AI Summary

This work proposes a novel paradigm—Vibe-driven Model-Driven Engineering (MDE)—to address the complexity of traditional MDE modeling and the insufficient reliability and maintainability of Vibe Coding. By integrating the natural language-to-code generation capabilities of large language models (LLMs) with the formal modeling strengths of MDE, this approach synergistically combines low-code/no-code principles to enhance development efficiency while ensuring system reliability. The paper systematically delineates the core concepts, applicable scenarios, key challenges, and emerging opportunities of this paradigm, offering both a theoretical foundation and practical pathways for the rapid development of highly reliable, complex software systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, etc. bring new challenges that we need to handle. In the last years, model-driven engineering (MDE), including its latest incarnation, i.e. low/no-code development, has been key to improving the quality and productivity of software development, but models themselves are becoming increasingly complex to specify and manage. At the same time, we are witnessing the growing popularity of vibe coding approaches that rely on Large Language Models (LLMs) to transform natural language descriptions into running code at the expense of potential code vulnerabilities, scalability issues and maintainability concerns. While many may think vibe coding will replace model-based engineering, in this paper we argue that, in fact, the two approaches can complement each other and provide altogether different development paths for different types of software systems, development scenarios, and user profiles. In this sense, we introduce the concept of \textit{vibe-driven model-based engineering} as a novel approach to integrate the best of both worlds (AI and MDE) to accelerate the development of reliable complex systems. We outline the key concepts of this new approach and highlight the opportunities and open challenges it presents for the future of software development.

Problem

• model-driven engineering
• vibe coding
• Large Language Models
• software complexity
• development methodology

Innovation

• vibe-driven model-based engineering
• model-driven engineering
• vibe coding
• Large Language Models
• software development