Journal Article Luca Berardinelli, Vittoriano Muttillo, Romina Eramo, Hugo Bruneliere, Abbas Rahimi, Antonio Cicchetti, Joan Giner-Miguelez, Abel Gómez, Pasqualina Potena, Mehrdad Saadatmand Model Driven Engineering, Artificial Intelligence, and DevOps for Software and Systems Engineering: A Systematic Mapping Study of Synergies and Challenges In: ACM Trans. Softw. Eng. Methodol., 2025, ISSN: 1049-331X, (Just Accepted). Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cloud Computing, Continuous Integration, Cyber-Physical Systems, DevOps, Internet of Things, Machine learning, Model-Driven Engineering @article{10.1145/3759454,
title = {Model Driven Engineering, Artificial Intelligence, and DevOps for Software and Systems Engineering: A Systematic Mapping Study of Synergies and Challenges},
author = { Luca Berardinelli and Vittoriano Muttillo and Romina Eramo and Hugo Bruneliere and Abbas Rahimi and Antonio Cicchetti and Joan Giner-Miguelez and Abel G\'{o}mez and Pasqualina Potena and Mehrdad Saadatmand},
url = {https://doi.org/10.1145/3759454},
doi = {10.1145/3759454},
issn = {1049-331X},
year = {2025},
date = {2025-08-01},
urldate = {2025-08-01},
journal = {ACM Trans. Softw. Eng. Methodol.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {This paper presents a systematic mapping study classifying existing scientific contributions on synergies of Model Driven Engineering (MDE), Artificial Intelligence/Machine Learning (AI/ML), and DevOps, with the overall objective of supporting the continuous development of Cyber-Physical Systems (CPSs). We collected papers from bibliographic sources and selected primary studies to analyse. Then, we characterised and classified the current state of the art, focusing on 1) main aspects already tackled at the intersection of at least two of the three studied areas, and 2) findings emerging from the analysis as a framework for potential future research, notably regarding the integration of the three studied areas. The results reveal that few approaches combine MDE, AI/ML, and DevOps for software and systems engineering. In contrast, several approaches have combined two of them, specifically MDE and DevOps. Approaches combining AI/ML with MDE or DevOps are also becoming more frequent and will most likely continue to progress in the future. These synergies cover a range of engineering activities, from requirements and design to monitoring, maintenance, and evolution. Open research challenges include advancing AI/ML, MDE, and DevOps integration, supporting scalable, data-oriented solutions, proposing new continuous engineering methods, and adapting DevOps practices to diverse systems.},
note = {Just Accepted},
keywords = {Artificial Intelligence, Cloud Computing, Continuous Integration, Cyber-Physical Systems, DevOps, Internet of Things, Machine learning, Model-Driven Engineering},
pubstate = {published},
tppubtype = {article}
}
This paper presents a systematic mapping study classifying existing scientific contributions on synergies of Model Driven Engineering (MDE), Artificial Intelligence/Machine Learning (AI/ML), and DevOps, with the overall objective of supporting the continuous development of Cyber-Physical Systems (CPSs). We collected papers from bibliographic sources and selected primary studies to analyse. Then, we characterised and classified the current state of the art, focusing on 1) main aspects already tackled at the intersection of at least two of the three studied areas, and 2) findings emerging from the analysis as a framework for potential future research, notably regarding the integration of the three studied areas. The results reveal that few approaches combine MDE, AI/ML, and DevOps for software and systems engineering. In contrast, several approaches have combined two of them, specifically MDE and DevOps. Approaches combining AI/ML with MDE or DevOps are also becoming more frequent and will most likely continue to progress in the future. These synergies cover a range of engineering activities, from requirements and design to monitoring, maintenance, and evolution. Open research challenges include advancing AI/ML, MDE, and DevOps integration, supporting scalable, data-oriented solutions, proposing new continuous engineering methods, and adapting DevOps practices to diverse systems. |