Towards Explainable Stakeholder-Aware Requirements Prioritisation in Aged-Care Digital Health

TL;DR

This study combines explainable machine learning and qualitative interviews to identify key human factors influencing requirements prioritization in aged-care digital health, highlighting stakeholder misalignments.

Contribution

It introduces a human-centric RE framework that integrates ML importance rankings with stakeholder validation to improve inclusivity.

Findings

Key human aspects significantly influence requirement priorities.

Stakeholder groups show substantial misalignment in perspectives.

Explainable ML effectively identifies human factors impacting prioritization.

Abstract

Requirements engineering for aged-care digital health must account for human aspects, because requirement priorities are shaped not only by technical functionality but also by stakeholders' health conditions, socioeconomics, and lived experience. Knowing which human aspects matter most, and for whom, is critical for inclusive and evidence-based requirements prioritisation. Yet in practice, while some studies have examined human aspects in RE, they have largely relied on expert judgement or model-driven analysis rather than large-scale user studies with meaningful human-in-the-loop validation to determine which aspects matter most and why. To address this gap, we conducted a mixed-methods study with 103 older adults, 105 developers, and 41 caregivers. We first applied an explainable machine learning to identify the human aspects most strongly associated with requirement priorities across 8 aged-care digital health themes, and then conducted 12 semi-structured interviews to validate and interpret the quantitative patterns. The results identify the key human aspects shaping requirement priorities, reveal their directional effects, and expose substantial misalignment across stakeholder groups. Together, these findings show that human-centric requirements analysis should engage stakeholder groups explicitly rather than collapsing their perspectives into a single aggregate view. This paper contributes an identification of the key human aspects driving requirement priorities in aged-care digital health and an explainable, human-centric RE framework that combines ML-derived importance rankings with qualitative validation to surface the stakeholder misalignments that inclusive requirements engineering must address.