Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance for the engineering of complex systems. In previous work, a model-based engineering approach integrating the formalization of machine learning tasks using the general-purpose modeling language SysML is presented. However, formalized machine learning tasks still require the implementation in a specialized programming languages like Python. Therefore, this work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks by integrating model transformation to generate executable code. The method focuses on the modifiability and maintainability of the model transformation so that extensions and changes to the code generation can be integrated without requiring modifications to the code generator. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Based thereon, quality attributes of model transformations are assessed and discussed. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation. Further, the work builds a theoretical basis for standardizing data-driven engineering implementation in practice.

翻译：数据驱动工程指的是利用机器学习系统性地收集和处理数据以改进工程系统。目前，数据驱动工程的实现依赖于基础数据科学和软件工程技能。与此同时，基于模型的工程在复杂系统工程中日益凸显其重要性。先前研究提出了一种基于模型工程的方案，通过通用建模语言SysML实现机器学习任务的形式化。然而，形式化的机器学习任务仍需通过Python等专业编程语言实现。因此，本研究旨在通过扩展先前机器学习任务形式化研究，集成模型转换以生成可执行代码，从而促进数据驱动工程在实践中的落地应用。该方法重点关注模型转换的可修改性和可维护性，使得代码生成的扩展和变更无需修改代码生成器即可集成。通过天气预测案例研究评估了所提出方法的可行性，并基于此对模型转换的质量属性进行了评估与讨论。结果表明该方法具有灵活性和简洁性，可减少实施工作量。此外，该工作为实践中数据驱动工程实施的标准化奠定了理论基础。