stakeholder management

This study addresses the challenges faced by SAE Level 4 autonomous driving systems in handling internal and external disturbances and faults, which are often exacerbated by a lack of stakeholder consensus on performance metrics and interface requirements, leading to non-traceable architectural decisions and inefficient communication. To overcome these issues, this work proposes a process-oriented engineering methodology that employs structured steps to harmonize multi-stakeholder requirements, explicitly define the performance metrics and interface specifications necessary for self-awareness and self-adaptation capabilities, and systematically integrate traceability and knowledge transfer mechanisms into the architecture design process. Validated within the autotech.agil project, the approach significantly enhances requirement consistency, decision transparency, and collaboration efficiency, while yielding key practical insights and directions for future improvement.

This study identifies a structural misalignment between stakeholder involvement (SHI) practices in commercial software development and the objectives of responsible AI (rAI): current SHI—oriented toward customer value and regulatory compliance—fails to support rAI’s core tenets of stakeholder empowerment, proactive risk anticipation, and public oversight. Employing a mixed-methods approach—including systematic analysis of 56 rAI guidelines, 130 practitioner surveys, and 10 in-depth interviews—we conducted cross-dimensional thematic coding and comparative analysis. Our findings constitute the first empirical evidence that existing SHI frameworks largely neglect rAI’s essential requirements. We identify four key impediments: institutional voids, role ambiguity, temporal misalignment, and ineffective evaluation mechanisms. Building on these insights, we propose an actionable intervention framework spanning institutional design, process integration, and multi-dimensional assessment. This work provides critical empirical grounding and a practical implementation roadmap for advancing rAI from principle to practice.

Software requirements are often implicit in stakeholder interviews, making them difficult to capture explicitly yet critically important for system design. This work proposes LENS, a novel approach that leverages context-aware large language models (LLMs) to jointly extract explicit requirements and infer implicit ones from interview transcripts, while incorporating organizational context to generate traceable user stories. LENS enables unified modeling and traceability of both explicit and implicit requirements. Evaluated on 12 interview transcripts from the cybersecurity domain, the method achieves an F1 score of 84.4% in explicit requirement extraction, and 75% of the inferred implicit requirements were rated by domain experts as practically valuable, demonstrating its potential to support automation and reduce manual analysis effort.

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.

This study addresses the semantic gap between stakeholder subjective contexts and formal system architectures by proposing an integrated approach that combines Soft Systems Methodology (SSM) with SysML v2. Leveraging KerML’s precise semantics and SysML v2’s native support for the ISO/IEC/IEEE 42010 standard, the authors construct a traceable reference architecture. The method systematically maps SSM outputs—such as stakeholder perspectives and concerns—onto core SysML v2 constructs, enabling a structured transformation from informal contextual understanding to formal architectural representation. Empirical validation through a case study demonstrates that this integration significantly enhances semantic consistency and reduces the risk of requirement misinterpretation. The work thus establishes a novel paradigm for aligning contextual insights with formal architectures in complex systems engineering.

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.

This study addresses the challenge of privacy communication in human–robot collaboration systems within Industry 5.0, where sensitive data monitoring raises significant privacy concerns that are often obscured by technical complexity, leading to mistrust and resistance among non-technical stakeholders. To bridge this gap, the authors propose a novel conceptual framework that integrates Privacy by Design principles with large language models (LLMs), leveraging LLMs for the first time in the requirements engineering process to automatically generate natural-language privacy reports tailored for non-technical audiences from representative human–robot monitoring scenarios. Evaluation across two industrial use cases demonstrates that the approach substantially enhances the comprehensibility of privacy information and supports informed decision-making, thereby addressing a critical accessibility gap in existing privacy communication mechanisms.

This work addresses the lack of systematic support for sustainability requirements in contemporary software requirements engineering, which hinders their effective integration with functional and non-functional requirements. To bridge this gap, the paper introduces JI-RADAR, the first open-source plugin integrated into the widely used project management platform Atlassian Jira. By extending Jira’s native architecture, JI-RADAR enables modeling, tracing, and visualization of sustainability requirements within existing development workflows. The tool seamlessly embeds sustainability considerations into standard practices, significantly enhancing teams’ ability to identify, analyze, and report on sustainability-related metrics during the requirements phase. This contribution fills a critical void in industrial practice by providing practical tooling for sustainability-aware requirements engineering.

This study investigates how to effectively integrate artificial intelligence with stakeholder collaboration to enhance the quality and efficiency of requirements elicitation. Through a mixed-methods controlled experiment, four approaches were compared: traditional collaborative elicitation, direct generation by large language models (LLMs), LLM-based generation from discussion transcripts, and a novel hybrid method that synthesizes stakeholder collaboration with LLM augmentation. The results demonstrate that the hybrid approach is the first to empirically yield significantly higher-quality requirements artifacts—producing documentation that is clearer, more actionable, and better aligned with user needs—while also improving participants’ experience of the elicitation process. These findings underscore the unique value of human-AI collaboration in requirements engineering, outperforming both purely manual and purely AI-driven methods.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.