The rise of machine learning (ML) and its embedding in systems has drastically changed the engineering of software-intensive systems. Traditionally, software engineering focuses on manually created artifacts such as source code and the process of creating them, as well as best practices for integrating them, i.e., software architectures. In contrast, the development of ML artifacts, i.e. ML models, comes from data science and focuses on the ML models and their training data. However, to deliver value to end users, these ML models must be embedded in traditional software, often forming complex topologies. In fact, ML-enabled software can easily incorporate many different ML models. While the challenges and practices of building ML-enabled systems have been studied to some extent, beyond isolated examples, little is known about the characteristics of real-world ML-enabled systems. Properly embedding ML models in systems so that they can be easily maintained or reused is far from trivial. We need to improve our empirical understanding of such systems, which we address by presenting the first large-scale study of real ML-enabled software systems, covering over 2,928 open source systems on GitHub. We classified and analyzed them to determine their characteristics, as well as their practices for reusing ML models and related code, and the architecture of these systems. Our findings provide practitioners and researchers with insight into practices for embedding and integrating ML models, bringing data science and software engineering closer together.

本研究解决了目前对机器学习（ML）驱动的软件系统特征了解不足的问题。通过对2928个开源系统的分类与分析，提出了一种新的研究思路，揭示了ML模型的嵌入与集成实践。研究结果为从业者和研究人员提供了重要见解，促进数据科学与软件工程的结合。

机器学习驱动的软件系统中模型集成的大规模研究