Mapping Traditional Software Non-Functional Requirements into the Machine Learning Context

Non-Functional Requirements (NFRs) are quality-focused attributes of a system that impact functional components. There are 24 common NFR classes with some of the most used being performance, scalability, availability, reliability, and security. The implementations of these classes are ambiguous and describe the attributes on the behavior of software. Due to their nature, NFRs are typically realized through the specification and implementation of functional requirements. For traditional software systems, the NFR definitions are well known. However, in Machine Learning (ML), which has numerous applications across a multitude of domains, a current challenge is that traditional software non-functional class definitions do not consider stochasticity and black-box nature of ML, and thus, are not appropriately reflecting the ML context. This paper aims to address that challenge by mapping traditional software NFR definitions into ML-NFR definitions by redefining them with ML characteristics in consideration. This research separates the 24 common NFRs into system NFRs and ML NFRs to delineate which of the original 24 carries over to the ML context. Because of the proliferation of ML, requirement engineers need to be cognizant of the nuances of ML behavior as it pertains to NFRs. This paper presents a mapping of NFRs into the ML context including new definitions, as needed, for NFRs in ML systems.