Operationalization-Based Modeling of Software Non-Functional Requirement Relationships

A Context-Aware Approach

Abstract

Software Non-Functional Requirements (NFRs), also known as Quality Attributes (QAs), such as security, performance,
and usability, play a critical role in shaping software architecture. However, their relationships are often complex, context-dependent, and difficult to anticipate early in design. Misunderstanding these relationships can lead to architectural conflicts, costly redesigns, and degraded system quality. Existing approaches, including Quality Attribute Relationship Models (QARMs), rely heavily on manual construction, resulting in static and incomplete representations that struggle to capture evolving domains or emerging evidence. This paper presents a semi-automated framework that leverages contextualized language models to identify supporting, conflicting, or neutral NFR relationships. While relationship classification and confidence scoring are performed automatically using a SciBERT-based contextual encoder, certain steps such as QA pair identification, operationalization extraction, and expert validation, require manual oversight to ensure accuracy and domain alignment. Using 3,200 research abstracts
from major software engineering digital libraries, we construct QA–QA pairs and generate operationalization-aware
relationship classifications, validated by domain experts. The approach achieves an F1 score of 0.83, demonstrating the
effectiveness of combining automated modeling with targeted manual input. These results support adaptive, evidence-driven QARMs for informed architectural decision-making and proactive management of potential NFR conflicts.