Automation in Model-Driven Engineering: A look back, and ahead

2024-05-28

ACM Transactions on Software Engineering and Methodology

AI Summary

This study addresses the insufficient automation capability of Model-Driven Engineering (MDE) and the diminishing role of engineers in the AI era. Methodologically, it synthesizes metamodeling, AI-assisted modeling, model transformation, formal verification, and human factors engineering to establish a human–machine collaborative modeling paradigm, featuring multi-granularity model coordination and real-time feedback mechanisms. Key contributions include: (1) the first systematic identification of three fundamental bottlenecks—semantic gap, dynamic adaptability, and trustworthiness with explainability; (2) the distillation of six emerging engineering activities requiring automation across the full system lifecycle; and (3) the proposal of a theoretically grounded, industrially viable framework and research roadmap for intelligent MDE evolution, which balances automation efficacy with sustained engineer agency and domain expertise.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.

Problem

• Improving Model-Driven Engineering with AI
• Automating unsupported engineering activities
• Balancing model use and engineer involvement

Innovation

• AI enhances Model-Driven Engineering
• Automation improves design-implementation transition
• Keeping engineers in complex system loops