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Mapping Traditional Software Non-Functional Requirements into the Machine Learning Context
Research Article  ·  Published: 09 May 2026
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ICCK Journal of Software Engineering
Volume 2, Issue 2, 2026: 102-120
Research Article Open Access

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

by
Timothy Elvira Timothy Elvira 1
Lynn Vonderhaar Lynn Vonderhaar 1 * ORCID
Juan Couder Juan Couder 1 ORCID
Tyler Thomas Procko Tyler Thomas Procko 1 ORCID
William Pate William Pate 1
Omar Ochoa Omar Ochoa 1 ORCID
1 Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach 32114, United States
* Corresponding Author: Lynn Vonderhaar, [email protected]
Volume 2, Issue 2
Volume 2, Issue 2 2026
Received: 25 March 2026 Accepted: 27 April 2026 Published: 09 May 2026 CrossMark
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DOI: 10.62762/JSE.2026.908327
ARK: ark:/57805/jse.2026.908327

Article Information

Published in ICCK Journal of Software Engineering
Volume/Issue Volume 2, Issue 2, 2026
Pages 102-120

Abstract

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.

Graphical Abstract

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

Keywords

machine learning non-functional requirements requirements engineering ML engineering MLOps

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Cite This Article

APA Style
Elvira, T., Vonderhaar, L., Couder, J., Procko, T. T., Pate, W., & Ochoa, O. (2026). Mapping Traditional Software Non-Functional Requirements into the Machine Learning Context. ICCK Journal of Software Engineering, 2(2), 102-120. https://doi.org/10.62762/JSE.2026.908327
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TY  - JOUR
AU  - Elvira, Timothy
AU  - Vonderhaar, Lynn
AU  - Couder, Juan
AU  - Procko, Tyler Thomas
AU  - Pate, William
AU  - Ochoa, Omar
PY  - 2026
DA  - 2026/05/09
TI  - Mapping Traditional Software Non-Functional Requirements into the Machine Learning Context
JO  - ICCK Journal of Software Engineering
T2  - ICCK Journal of Software Engineering
JF  - ICCK Journal of Software Engineering
VL  - 2
IS  - 2
SP  - 102
EP  - 120
DO  - 10.62762/JSE.2026.908327
UR  - https://www.icck.org/article/abs/JSE.2026.908327
KW  - machine learning
KW  - non-functional requirements
KW  - requirements engineering
KW  - ML engineering
KW  - MLOps
AB  - 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.
SN  - 3069-1834
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Elvira2026Mapping,
  author = {Timothy Elvira and Lynn Vonderhaar and Juan Couder and Tyler Thomas Procko and William Pate and Omar Ochoa},
  title = {Mapping Traditional Software Non-Functional Requirements into the Machine Learning Context},
  journal = {ICCK Journal of Software Engineering},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {102-120},
  doi = {10.62762/JSE.2026.908327},
  url = {https://www.icck.org/article/abs/JSE.2026.908327},
  abstract = {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.},
  keywords = {machine learning, non-functional requirements, requirements engineering, ML engineering, MLOps},
  issn = {3069-1834},
  publisher = {Institute of Central Computation and Knowledge}
}

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