A large-scale empirical study based on an industrial dataset from Infineon, comprising 8,082 stakeholder requirements and 5,870 product requirements enriched with traceability links, decision outcomes, deviation rationales, and domain references, which provides concrete insight into industrial requirements intake and refinement practices.
Abstract
The automotive industry's shift toward software-driven systems has increased system complexity and raised the importance of effective requirement intake and refinement for correctness, compliance, development speed, and systematic reuse. Although prior research has proposed techniques for improving requirement quality, limited empirical evidence exists on how stakeholder-level requirements are evaluated, refined, and transformed into product-level requirements in industrial automotive practice. This paper presents a large-scale empirical study based on an industrial dataset from Infineon, comprising 8,082 stakeholder requirements and 5,870 product requirements enriched with traceability links, decision outcomes, deviation rationales, and domain references. Using a mixed-methods approach, we combine quantitative analyses of requirement structures, decision distributions, and mapping patterns with qualitative analyses of rationales, referenced specifications, and software- and hardware-related artifacts. We investigate structural and contextual differences between stakeholder and product requirements, factors influencing acceptance, rejection, and approval with deviation, and the nature of stakeholder-to-product refinement. The results reveal systematic differences across abstraction levels and show that refinement complexity is driven primarily by architectural scope and missing contextual information rather than linguistic verbosity. We further derive a taxonomy of stakeholder-product mapping patterns and relate these patterns to differing refinement effort. The findings provide concrete insight into industrial requirements intake and refinement practices and identify actionable opportunities for improving intake validation, deviation management, and tool-supported contextual enrichment to support faster and more reusable automotive product development.
Modern interactive products require tighter integration between UX research and engineering design. However, the translation of scenario-based UX representations into engineering specifications is often informal, weakening traceability and making it harder to preserve the contextual rationale for technical decisions. We conceptualize this problem as the experience–specification gap: a representational discontinuity between experiential user scenarios and formal engineering representations. To address it, we develop Scenario-Oriented Specification (SOS), a five-step design method that structures translation from user scenarios to engineering specifications through linked artifacts anchored by persistent identifiers. SOS is grounded in five meta-requirements and three design principles. The method was evaluated through longitudinal enactment in an interdisciplinary industrial development project across 15 user scenarios and four predefined dimensions: practical enactability, output quality, process quality, and process efficiency. The results show that SOS supported traceable specification development while making under-specification and feasibility constraints explicit, and that a sixth step, spatial integration, emerged during enactment as an extension needed in this embodied product context. The study contributes to engineering design research by conceptualizing the experience–specification gap as a representational problem, introducing SOS as a reusable representational translation logic, and demonstrating a combined empirical and structural validation approach for evaluating design methods under realistic industrial conditions.
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Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-centered approaches to support explainability, there is limited empirical understanding of how existing Requirements Engineering (RE) practices support explainability requirements across the RE lifecycle, especially in an industrial context. This paper reports early findings from an ongoing industry-based study investigating how explainability requirements are elicited, specified, and validated using established RE techniques. We conducted a multi-phase qualitative study with eight practitioners at Daimler Truck, employing think-aloud protocols and moderated group discussions across requirements elicitation, specification, and validation steps. Our preliminary analysis reveals recurring challenges across all steps, including conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty. These findings indicate that current RE practices provide limited support to systematically address explainability requirements. The paper contributes empirical insights into step-specific and cross-cutting challenges and outlines a research vision toward developing an empirically grounded RE framework for explainable AI-based systems.
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The objective of this research is to obtain feedback from industry experts to understand the challenges related to the employability of future-ready software engineering undergraduate students and employs a qualitative study to gather the views of the experts.
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The results indicate that language-model assistance can add value in regulated automotive requirements workflows when it is embedded in deterministic, provenance-rich, and expert-governed processes.
