Exploring Use of Generative AI for Representing and Reasoning with Non-Functional Requirements

Non-functional requirements (NFRs), such as usability, safety, security, and the like, describe how well the software system should do what it’s intended to do functionally, and, as such, should be addressed well. However, addressing NFRs well is not easy. In this talk, I will explore using Generative AI for facilitating representation and reasoning with NFRs, at a time when interest in utilizing Machine Leaning/Generative AI seems to be spreading like a wildfire in many industries and research areas. Unsurprisingly, software engineering more broadly, and requirements engineering more specifically, seems no exception to this. After discussing a problem-driven, ontology-based approach to question (prompt) engineering, I will briefly describe some critical challenges going forward at the end.

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Publication Details

Published
2025-05-29
DOI
https://doi.org/10.1109/sera65747.2025.11154600
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
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article

Exploring Use of Generative AI for Representing and Reasoning with Non-Functional Requirements

Lawrence Chung
AI-based Problem Solving and Planning
article

Exploring Use of Generative AI for Representing and Reasoning with Non-Functional Requirements

Lawrence Chung
article en

Abstract

Non-functional requirements (NFRs), such as usability, safety, security, and the like, describe how well the software system should do what it’s intended to do functionally, and, as such, should be addressed well. However, addressing NFRs well is not easy. In this talk, I will explore using Generative AI for facilitating representation and reasoning with NFRs, at a time when interest in utilizing Machine Leaning/Generative AI seems to be spreading like a wildfire in many industries and research areas. Unsurprisingly, software engineering more broadly, and requirements engineering more specifically, seems no exception to this. After discussing a problem-driven, ontology-based approach to question (prompt) engineering, I will briefly describe some critical challenges going forward at the end.

Software Engineering Institute (US)
Openalex Percentile: Top 87%
AI-based Problem Solving and Planning
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