论文标题
机器学习的非功能要求：探索系统范围和兴趣
Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest
论文作者
论文摘要
与传统系统相比，依靠机器学习（ML系统）的系统对系统质量的需求不同。这种质量需求（称为非功能性要求（NFR））的定义，范围和重要性可能有所不同，而NFR对于传统系统的定义，范围和重要性。尽管NFR对ML系统的重要性很重要，但与我们在传统领域中的理解相比，我们对它们的定义和范围以及每个NFR的现有研究程度的理解缺乏。 Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results -- as well as inter-coder reliability on a sample of NFRs -- to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each示例以定义系统部分的NFR范围（例如训练数据，ML模型或其他系统元素）。这些最初的发现构成了这个新兴领域中未来研究的基础。
Systems that rely on Machine Learning (ML systems) have differing demands on system quality compared to traditional systems. Such quality demands, known as non-functional requirements (NFRs), may differ in their definition, scope, and importance from NFRs for traditional systems. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope -- and of the extent of existing research in each NFR -- is lacking compared to our understanding in traditional domains. Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results -- as well as inter-coder reliability on a sample of NFRs -- to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model, or other system elements). These initial findings form the groundwork for future research in this emerging domain.
