Next-Gen Software Engineering. Big Models for AI-Augmented Model-Driven Software Engineering

2024-09-26

AI Summary

Model-Driven Software Engineering (MDSE) faces practical adoption barriers due to high modeling costs and steep domain expertise requirements. Method: This paper proposes an AI-augmented “paired modeling” paradigm, deeply integrating MDSE with large language models (LLMs). It synergizes LLMs, code corpus analytics, and established model-driven standards (e.g., UML, MOF) to establish AI4SE—the first AI capability taxonomy tailored for software engineering—and systematically defines collaborative modeling pathways wherein models and AI mutually enhance each other. Contribution/Results: (1) The AI4SE taxonomy provides a foundational classification of AI capabilities for SE tasks; (2) the paired modeling paradigm formalizes bidirectional reinforcement between models and AI; (3) it delivers a theoretical foundation and evolutionary roadmap for industrial-grade intelligent modeling toolchains, substantially lowering modeling entry barriers and elevating automation levels.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

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: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The effectiveness of model-driven software engineering (MDSE) has been successfully demonstrated in the context of complex software; however, it has not been widely adopted due to the requisite efforts associated with model development and maintenance, as well as the specific modelling competencies required for MDSE. Concurrently, artificial intelligence (AI) methods, particularly deep learning methods, have demonstrated considerable abilities when applied to the huge code bases accessible on open-source coding platforms. The so-called big code provides the basis for significant advances in empirical software engineering, as well as in the automation of coding processes and improvements in software quality with the use of AI. The objective of this paper is to facilitate a synthesis between these two significant domains of software engineering (SE), namely models and AI in SE. The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, AI4SE. In light of the aforementioned considerations, a vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development. Finally, the new paradigm of pair modelling in MDSE is proposed.

Problem

• Enhancing MDSE with AI integration
• Automating coding processes using big data
• Developing AI-assisted Big Models in SE

Innovation

• AI-augmented model-driven software engineering
• Big Models for software development
• Pair modelling in MDSE