A Systematic Literature Review of Model-Driven Engineering Using Machine Learning

Model-driven engineering (MDE) is a software engineering paradigm based on the systematic use of models. Over the past few years, engineers have significantly increased the use of MDE, which has been reported as a successful paradigm for developing industrial software. Recently, there have also been remarkable advancements in the Artificial Intelligence (AI) domain, with a significant increase in advanced Machine Learning (ML) techniques. The advances in both fields have led to a surge in works that dwell within the intersection of ML and MDE. This work places the focus on systematically reviewing works that leverage ML to solve MDE problems. We have reviewed a total of 9,194 papers, selecting 98 studies for further analysis. The results of our Systematic Literature Review (SLR) bring light to the current state of the art and trends in the field, discussing the drift in the usage of the different available ML techniques along with the remaining research gaps and open challenges. Our SLR has the potential to produce a positive impact in the research community by steering it towards ML techniques that have been successfully applied to solve MDE challenges.

I. Introduction

Model-driven engineering (MDE) is a software engineering paradigm based on the systematic use of models as primary artifacts throughout the software development cycle [1]. With the MDE paradigm, models are used to capture and design the characteristics of software systems: models can be run and interpreted at run-time, automatically transformed into code, or used to design and derive other software artifacts. Major players in the software engineering field and in the requirements engineering field foresee a broad adoption of MDE techniques [2], [3], since they improve the productivity, quality, and performance of software in industrial scenarios that require more abstract approaches than mere coding [1].