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Capturing Dependencies Within Machine Learning via a Formal Process Model

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Abstract

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.

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Author information

Authors and Affiliations

  1. Mobile and Distributed Systems Group, LMU Munich, Munich, Germany

    Fabian Ritz, Thomy Phan, Andreas Sedlmeier, Philipp Altmann, Claudia Linnhoff-Popien & Thomas Gabor

  2. Technology, Siemens AG, Munich, Germany

    Jan Wieghardt, Reiner Schmid, Horst Sauer & Cornel Klein

Authors
  1. Fabian Ritz

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  2. Thomy Phan

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  3. Andreas Sedlmeier

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  4. Philipp Altmann

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  5. Jan Wieghardt

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  6. Reiner Schmid

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  7. Horst Sauer

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  8. Cornel Klein

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  9. Claudia Linnhoff-Popien

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  10. Thomas Gabor

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Corresponding author

Correspondence toFabian Ritz.

Editor information

Editors and Affiliations

  1. University of Limerick, CSIS and Lero, Limerick, Ireland

    Tiziana Margaria

  2. TU Dortmund, Dortmund, Germany

    Bernhard Steffen

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Ritz, F.et al. (2022). Capturing Dependencies Within Machine Learning via a Formal Process Model. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_16

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