Development of machine learning (ML) applications is hard. Producing successful applications requires, among others, being deeply familiar with a variety of complex and quickly evolving application programming interfaces (APIs). It is therefore critical to understand what prevents developers from learning these APIs, using them properly at development time, and understanding what went wrong when it comes to debugging. We look at the (lack of) guidance that currently used development environments and ML APIs provide to developers of ML applications, contrast these with software engineering best practices, and identify gaps in the current state of the art. We show that current ML tools fall short of fulfilling some basic software engineering gold standards and point out ways in which software engineering concepts, tools and techniques need to be extended and adapted to match the special needs of ML application development. Our findings point out ample opportunities for research on ML-specific software engineering.

研究指出，现有的机器学习开发环境和 API 缺乏足够的指导及软件工程最佳实践，需要在专门开发的机器学习应用程序开发方面扩展和适应软件工程概念、工具和技术，并为机器学习特定的软件工程提供充足的研究机会。

应用软件工程技术实现应用机器学习的指导