Model-driven Engineering for Machine Learning Components: A Systematic
  Literature Review
Hira Naveed Chetan Arora Hourieh Khalajzadeh John Grundy Omar Haggag
Context: Machine Learning (ML) has become widely adopted as a component in
many modern software applications. Due to the large volumes of data available,
organizations want to increasingly leverage their data to extract meaningful
insights and enhance business profitability. ML components enable predictive
capabilities, anomaly detection, recommendation, accurate image and text
processing, and informed decision-making. However, developing systems with ML
components is not trivial; it requires time, effort, knowledge, and expertise
in ML, data processing, and software engineering. There have been several
studies on the use of model-driven engineering (MDE) techniques to address
these challenges when developing traditional software and cyber-physical
systems. Recently, there has been a growing interest in applying MDE for
systems with ML components. Objective: The goal of this study is to further
explore the promising intersection of MDE with ML (MDE4ML) through a systematic
literature review (SLR). Through this SLR, we wanted to analyze existing
studies, including their motivations, MDE solutions, evaluation techniques, key
benefits and limitations. Results: We analyzed selected studies with respect to
several areas of interest and identified the following: 1) the key motivations
behind using MDE4ML; 2) a variety of MDE solutions applied, such as modeling
languages, model transformations, tool support, targeted ML aspects,
contributions and more; 3) the evaluation techniques and metrics used; and 4)
the limitations and directions for future work. We also discuss the gaps in
existing literature and provide recommendations for future research.
Conclusion: This SLR highlights current trends, gaps and future research
directions in the field of MDE4ML, benefiting both researchers and
practitioners