TY - GEN
T1 - MDE for Machine Learning-Enabled Software Systems
T2 - 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
AU - Kirchhof, Jörg Christian
AU - Kusmenko, Evgeny
AU - Ritz, Jonas
AU - Rumpe, Bernhard
AU - Moin, Armin
AU - Badii, Atta
AU - Günnemann, Stephan
AU - Challenger, Moharram
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/23
Y1 - 2022/10/23
N2 - In this paper, we propose to adopt the MDE paradigm for the development of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain. We illustrate how two state-of-The-Art open-source modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as demonstrated through a case study. The case study illustrates using ML, in particular deep Artificial Neural Networks (ANNs), for automated image recognition of handwritten digits using the MNIST reference dataset, and integrating the machine learning components into an IoT-system. Subsequently, we conduct a functional comparison of the two frameworks, setting out an analysis base to include a broad range of design considerations, such as the problem domain, methods for the ML integration into larger systems, and supported ML methods, as well as topics of recent intense interest to the ML community, such as AutoML and MLOps. Accordingly, this paper is focused on elucidating the potential of the MDE approach in the ML domain. This supports the ML-engineer in developing the (ML/software) model rather than implementing the code, and additionally enforces reusability and modularity of the design through enabling the out-of-The-box integration of ML functionality as a component of the IoT or cyber-physical systems.
AB - In this paper, we propose to adopt the MDE paradigm for the development of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain. We illustrate how two state-of-The-Art open-source modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as demonstrated through a case study. The case study illustrates using ML, in particular deep Artificial Neural Networks (ANNs), for automated image recognition of handwritten digits using the MNIST reference dataset, and integrating the machine learning components into an IoT-system. Subsequently, we conduct a functional comparison of the two frameworks, setting out an analysis base to include a broad range of design considerations, such as the problem domain, methods for the ML integration into larger systems, and supported ML methods, as well as topics of recent intense interest to the ML community, such as AutoML and MLOps. Accordingly, this paper is focused on elucidating the potential of the MDE approach in the ML domain. This supports the ML-engineer in developing the (ML/software) model rather than implementing the code, and additionally enforces reusability and modularity of the design through enabling the out-of-The-box integration of ML functionality as a component of the IoT or cyber-physical systems.
KW - artificial intelligence
KW - domain specific modeling
KW - machine learning
KW - model-driven engineering
KW - tools
UR - http://www.scopus.com/inward/record.url?scp=85142937814&partnerID=8YFLogxK
U2 - 10.1145/3550356.3561576
DO - 10.1145/3550356.3561576
M3 - Conference contribution
AN - SCOPUS:85142937814
T3 - Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings
SP - 380
EP - 387
BT - Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
PB - Association for Computing Machinery, Inc
Y2 - 23 October 2022 through 28 October 2022
ER -