一句话：提出基于特征的机器学习开发生命周期分析方法，并建立跨阶段特征定义分类体系。
本文关注含机器学习组件系统的安全性与可信性，指出现有工作虽在 MLDL 各阶段使用“特征”这一高层抽象来表示功能、模型行为和数据，但不同社区对特征的理解缺乏统一。为支持对 MLDL 的软件工程分析，作者提出“基于特征的 MLDL 分析”。方法上，他们梳理并建立现有 MLDL 各阶段特征定义的分类体系，将不同阶段的特征相互映射。基于该分类，论文识别阶段间特征定义的空隙、未来研究方向，以及软件工程与其他 MLDL 专家潜在合作点。该工作属于立场与综述性贡献，强调以特征为共同语言促进跨阶段、跨社区的分析与协作。
The safety and trustworthiness of systems with components that are based on Machine Learning (ML) require an in-depth understanding and analysis of all stages in its Development Lifecycle (MLDL). High-level abstractions of desired functionalities, model behaviour, and data are called features, and they have been studied by different communities across all MLDL stages. In this paper, we propose to support Software Engineering analysis of the MLDL through features, calling it feature-based analysis of the MLDL. First, to achieve a shared understanding of features among different experts, we establish a taxonomy of existing feature definitions currently used in various MLDL stages. Through this taxonomy, we map features from different stages to each other, discover gaps and future research directions and identify areas of collaboration between Software Engineering and other MLDL experts.