论文标题
通过软件工程技术在应用机器学习中获得指导
Achieving Guidance in Applied Machine Learning through Software Engineering Techniques
论文作者
论文摘要
机器学习（ML）应用的开发很难。生产成功的应用需要，除其他外，还需要对各种复杂而快速发展的应用程序编程界面（API）非常熟悉。因此，重要的是要了解什么阻止开发人员学习这些API，在开发时正确使用它们，并了解调试时出了什么问题。我们查看当前使用开发环境和ML API为ML应用程序开发人员提供的（缺乏）指导，将这些指导与软件工程最佳实践进行对比，并确定当前最新技术状况的差距。我们表明，当前的ML工具无法满足一些基本的软件工程金标准，并指出了需要扩展和适应软件工程概念，工具和技术以符合ML应用程序开发的特殊需求的方法。我们的发现指出了有关ML特异性软件工程研究的足够机会。
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.
