# Model-driven Engineering for Machine Learning Components: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2311.00284 · HTML: https://mlsanity.com/paper/2311.00284/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 78 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models](https://mlsanity.com/paper/2111.13657/) (2021)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [AI and Ethics -- Operationalising Responsible AI](https://mlsanity.com/paper/2105.08867/) (2021)
- [A Survey on Domain-Specific Languages for Machine Learning in Big Data](https://mlsanity.com/paper/1602.07637/) (2016)
- [A Model-Driven Approach to Machine Learning and Software Modeling for the IoT](https://mlsanity.com/paper/2107.02689/) (2021)
- [Open science in machine learning](https://mlsanity.com/paper/1402.6013/) (2014)
- [Supporting AI Engineering on the IoT Edge through Model-Driven TinyML](https://mlsanity.com/paper/2107.02690/) (2021)
- [The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review](https://mlsanity.com/paper/2109.08094/) (2021)

## Built on by (most cited first, 7 of 7)

- [A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model](https://mlsanity.com/paper/2408.02920/) (2024)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [LoCoML: A Framework for Real-World ML Inference Pipelines](https://mlsanity.com/paper/2501.14165/) (2025)
- [Towards a Domain-Specific Modelling Environment for Reinforcement Learning](https://mlsanity.com/paper/2410.09368/) (2024)
- [Understanding Practitioners Perspectives on Monitoring Machine Learning Systems](https://mlsanity.com/paper/2509.25195/) (2025)
- [A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms](https://mlsanity.com/paper/2510.09308/) (2025)
- [A Model-Driven Approach for Developing Families of Reinforcement Learning Environments](https://mlsanity.com/paper/2606.20324/) (2026)

## Abstract (arXiv)

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