MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat

• URL: http://arxiv.org/abs/2209.07282v1
• Date: Thu, 15 Sep 2022 13:21:16 GMT
• Title: MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat
• Authors: J\"org Christian Kirchhof and Evgeny Kusmenko and Jonas Ritz and Bernhard Rumpe and Armin Moin and Atta Badii and Stephan G\"unnemann and Moharram Challenger
• Abstract summary: We propose to adopt the MDE paradigm for the development of Machine Learning-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.
• Score: 5.839906946900443
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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