Augmenting software engineering with AI and developing it further towards AI-assisted model-driven software engineering

TL;DR

This paper explores integrating AI techniques with model-driven software engineering to enhance automation, reduce effort, and leverage big code data, proposing a new taxonomy and a vision for AI-assisted SE.

Contribution

It introduces a taxonomy for AI-augmented software engineering and proposes a pair modelling paradigm to advance MDSE through AI integration.

Findings

Overview of AI-augmented software engineering

Development of ai4se taxonomy

Proposal of pair modelling paradigm

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 pair modelling paradigm is proposed for adoption by the MDSE industry.