Fusing Modeling and Programming into Language-Oriented Programming

In the development of computer-based systems, modelling is often advocated in addition to programming, in that it helps in reflecting the application domain and that it makes the design and experiment activities of development more efficient. However, there is disagreement about what models are and how they can be used in software systems development. In this paper, we present the Scandinavian approach to modelling, which makes a clear distinction between models and model descriptions. This paper explains the connections between models, descriptions, systems, and executions. Combining the Scandinavian approach with the Kiel notion of model, we establish that both descriptions and executions are closely connected instruments with different roles. The paper argues that (program) executions are the models of dynamic systems, not their descriptions in terms of diagrams and text. So in a general sense programming is about writing descriptions for systems. In particular the paper clarifies when programming is also modelling.

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“…Markus Völter has compared programming and modelling in [Völter, 2011] and [Voelter, 2018]. He uses a definition of model-driven as follows.…”

Section: Programming Versus Modellingmentioning

confidence: 99%

Models Versus Model Descriptions

Fischer

Møller-Pedersen

Prinz

et al. 2021

Modelling to Program

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“…Following the Model-Driven Development and Language-Oriented Programming approaches [10,11] we address functional extensibility (REQ1) by defining a separate model for the experiment. The specification of the experiment captures the structure of the experiment and serves as a basis for integration of external tools and data with the user code.…”

Section: Experiments Specificationmentioning

confidence: 99%

MLDev: Data Science Experiment Automation and Reproducibility Software

Khritankov¹,

Nikita²,

Ukhov³

et al. 2021

Preprint

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In this paper we explore the challenges of automating experiments in data science. We propose an extensible experiment model as a foundation for integration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.

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