This preprint specifies and defines all the Terms, Properties, and
Relationships of NFRsTDO (Non-Functional Requirements Top-Domain Ontology).
NFRsTDO v1.2, whose UML conceptualization is shown in Figure 1 is a slightly
updated version of its predecessor, namely NFRsTDO v1.1. NFRsTDO is an ontology
mainly devoted to quality (non-functional) requirements and quality/cost views,
which is placed at the top-domain level in the context of a multilayer
ontological architecture called FCD-OntoArch (Foundational, Core, Domain, and
instance Ontological Architecture for sciences). Figure 2 depicts its five
tiers, which entail Foundational, Core, Top-Domain, Low-Domain, and Instance.
Each level is populated with ontological components or, in other words,
ontologies. Ontologies at the same level can be related to each other, except
at the foundational level, where only ThingFO (Thing Foundational Ontology) is
found. In addition, ontologies' terms and relationships at lower levels can be
semantically enriched by ontologies' terms and relationships from the higher
levels. NFRsTDO's terms and relationships are mainly extended/reused from
ThingFO, SituationCO (Situation Core Ontology), ProcessCO (Process Core
Ontology), and FRsTDO (Functional Requirements Top-Domain Ontology).
Stereotypes are the used mechanism for enriching NFRsTDO terms. Note that
annotations of updates from the previous version (NFRsTDO v1.1) to the current
one (v1.2) can be found in Appendix A.

本文介绍了 NFRsTDO (Non-Functional Requirements Top-Domain Ontology) 的所有术语、属性和关系，它是专门针对质量 (非功能) 要求和质量 / 成本视图的本体，并放置在一个名为 FCD-OntoArch (sciences 的基础、核心、领域和实例本体架构) 的多层本体架构的顶级域层次中。

NFRsTDO v1.2 的术语、属性和关系 —— 一个顶级域的非功能需求本体论

NFRsTDO v1.2's Terms, Properties, and Relationships -- A Top-Domain Non-Functional Requirements Ontology

Nowadays, systems containing components based on machine learning (ML)
methods are becoming more widespread. In order to ensure the intended behavior
of a software system, there are standards that define necessary quality aspects
of the system and its components (such as ISO/IEC 25010). Due to the different
nature of ML, we have to adjust quality aspects or add additional ones (such as
trustworthiness) and be very precise about which aspect is really relevant for
which object of interest (such as completeness of training data), and how to
objectively assess adherence to quality requirements. In this article, we
present the construction of a quality model (i.e., evaluation objects, quality
aspects, and metrics) for an ML system based on an industrial use case. This
quality model enables practitioners to specify and assess quality requirements
for such kinds of ML systems objectively. In the future, we want to learn how
the term quality differs between different types of ML systems and come up with
general guidelines for specifying and assessing qualities of ML systems.

本文根据工业实例构建了一种基于机器学习系统的质量模型，使从业者能够客观地规定和评估此类机器学习系统的质量要求。