Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the “route” of transformations that a domain expert may choose to take while developing their solution.

To ground our conceptual framework in the state of the practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain specificity and machine learning usage of their problem, workflow, and implementation.

The state of the practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

领域专家越来越多地采用机器学习来解决特定领域的问题。本文向软件工程研究人员介绍了领域专家在通过计算工作流程和底层可执行实现解决问题时面临的六个关键挑战。这些挑战源于我们的概念框架，该框架提出了领域专家在开发解决方案时可能选择采取的转型“路线”。

为了将我们的概念框架建立在实践的基础上，本文讨论了一系列可用的文本和图形工作流程系统及其对我们框架中描述的转换的支持。还检查了各个领域文献中的示例研究，以突出显示领域专家使用的工具以及领域特异性的分类以及其问题、工作流程和实现的机器学习用法。

实践状况为我们对六个关键挑战的讨论提供了信息，我们在其中确定了可用工具无法充分解决哪些挑战和转型。我们还为软件工程研究人员提出了可能的研究方向，以提高这些工具的自动化程度，并在软件工程和各个科学领域之间传播最佳实践技术。