Publications

@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}

}

@article{Bruneliere:MICPRO:2022,

title = {AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber\textendashPhysical Systems},

author = {Hugo Bruneliere and Vittoriano Muttillo and Romina Eramo and Luca Berardinelli and Abel G\'{o}mez and Alessandra Bagnato and Andrey Sadovykh and Antonio Cicchetti},

doi = {10.1016/j.micpro.2022.104672},

issn = {0141-9331},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Microprocessors and Microsystems},

volume = {94},

pages = {104672},

abstract = {The advent of complex Cyber\textendashPhysical Systems (CPSs) creates the need for more efficient engineering processes. Recently, DevOps promoted the idea of considering a closer continuous integration between system development (including its design) and operational deployment. Despite their use being still currently limited, Artificial Intelligence (AI) techniques are suitable candidates for improving such system engineering activities (cf. AIOps). In this context, AIDOaRT is a large European collaborative project that aims at providing AI-augmented automation capabilities to better support the modeling, coding, testing, monitoring, and continuous development of CPSs. The project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable CPSs. The resulting framework will (1) enable the dynamic observation and analysis of system data collected at both runtime and design time and (2) provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases. This paper describes the main research objectives and underlying paradigms of the AIDOaRt project. It also introduces the conceptual architecture and proposed approach of the AIDOaRt overall solution. Finally, it reports on the actual project practices and discusses the current results and future plans.},

keywords = {AIOps, Artificial Intelligence, Continuous development, Cyber\textendashPhysical Systems, DevOps, Model Driven Engineering, Software engineering, System engineering},

pubstate = {published},

tppubtype = {article}

}