HM-Req: A Framework for Embedding Values within CPS Human Monitoring Requirements

TL;DR

HM-Req is a framework that uses a controlled natural language to capture and integrate stakeholder human values into CPS human monitoring requirements, enhancing ethical compliance and conflict resolution.

Contribution

It introduces a novel requirements elicitation framework with a CNL and a Value Dashboard for systematically capturing and managing human values in CPS monitoring requirements.

Findings

The CNL effectively captures diverse human monitoring requirements.

Validation shows HM-Req's usefulness in requirements elicitation.

Expert feedback confirms the framework's practical relevance.

Abstract

Monitoring humans, for example, their movement or location, is essential for safe and efficient human-machine collaboration in Cyber-Physical Systems (CPS). This information allows CPS to ensure safety properties, adapt their behaviour dynamically, and coordinate with humans. To ensure that the design of a CPS respects ethical principles and the privacy of its stakeholders, system requirements, particularly those related to human monitoring, must reflect the human values of all involved stakeholders. However, human values are often underrepresented in Software Engineering -- particularly during requirements elicitation and system design, crucial phases when introducing ethically critical functionality. Stakeholder values are often implicit and conflicting, yet rarely systematically captured. Furthermore, unstructured natural language requirements introduce ambiguity and vagueness, complicating conflict resolution. To address these problems, we propose HM-Req, a novel requirements elicitation framework including a Controlled Natural Language (CNL) for defining human monitoring requirements. These requirements are then augmented with human values from relevant stakeholders and integrated into a Value Dashboard to detect potential conflicts that require further discussion and resolution. Validation results, applying the CNL to different datasets and conducting a survey and expert interview, confirms the CNL's ability to capture diverse human monitoring requirements and show HM-Req's usefulness for requirements elicitation activities.