synthesize design principles

This work addresses the inefficiencies inherent in manual construction of reference and influence models within Design Research Methodology (DRM), including poor readability, difficulty in modification, and challenges in tracing evidential support. To overcome these limitations, the paper introduces DREAMS, a novel modeling environment that, for the first time, articulates DRM-specific modeling support requirements. DREAMS incorporates typed causal modeling and symbolic relationship representation, directly anchoring hypotheses, empirical inputs, and literature citations to causal links. It further integrates interactive layout optimization and efficient retrieval mechanisms. Preliminary user evaluations demonstrate that DREAMS significantly reduces model creation and revision time, minimizes node reordering and edge crossings, and enhances both evidential traceability and overall model maintainability.

This study addresses the prevalent ambiguity, inconsistency, and incompleteness in articulating explainability requirements for AI systems due to a lack of standardized specifications. Through a structured literature review and interviews with developers, the authors identify a set of explainability quality attributes, which are then refined via a large-scale survey of practitioners into ten core attributes. For the first time, these attributes are translated into a prioritized, actionable guideline for writing explainability requirements. Building on this foundation, the authors design a lightweight, iterative requirements engineering workflow augmented by a large language model to assist in requirement generation. An accompanying web-based tool reduces average requirement drafting time by 23.5%, and user evaluations indicate that the generated requirements match or slightly exceed manually written ones in terms of implementability and textual quality.

This study addresses the challenge in axiomatic design of accurately translating customer needs and constraints into a minimal and independent set of primary functional requirements (FRs). Focusing on the problem definition phase, it systematically elucidates the nature, invariance, and formulation principles of primary FRs. Building upon Nam P. Suh’s theoretical framework and integrating insights from complexity theory and requirements engineering, the work establishes—for the first time—the objectivity and uniqueness of primary FRs, clarifies common misconceptions, and critically examines the applicability boundaries of large language models in this context. The research provides designers with a clear, actionable methodology for constructing primary FRs, thereby significantly enhancing the rigor of problem definition and the likelihood of successful design outcomes.

Ambiguous definitions of Participatory Design (PD) have led to conceptual vagueness and unresolved concerns regarding design fairness. Method: We conducted a systematic literature review (SLR) of over 100 empirical PD studies, applying thematic coding and cross-case comparison. Contribution/Results: First, we identify—structurally and for the first time—five core leverage points (e.g., emergent vs. pre-specified design, direct vs. indirect participation) that mediate the relationship between PD processes and fairness outcomes, thereby establishing a theoretical framework linking PD practice to design fairness. Second, we catalog 14 concrete participatory techniques, revealing intangible system design as the dominant application domain and multi-stage recruitment with hybrid technique combinations as prevailing practices. Third, we clarify how stakeholders’ degree, timing, mode, and technical configuration of involvement shape fairness mechanisms. This work provides empirically grounded, actionable decision guidelines for advancing PD methodology and practice.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This study addresses the challenge that values and potential harms in early AI design are often too abstract or addressed too late to effectively inform decision-making. To bridge this gap, the authors employ a Research through Design (RtD) approach, integrating participatory design, card sorting, and Value Sensitive Design (VSD) to develop a toolkit comprising an AI capabilities library, 24 value–harm cards, and value–tension maps. This toolkit uniquely embeds values and harms into the initial stages of AI design through structured, visual representations, introducing “productive friction” to stimulate ethical reflection and explicitly surface value tensions to support deliberation. Evaluation via 30 questionnaires and 12 in-depth interviews demonstrates that the toolkit is clear and usable, significantly enhancing designers’ abilities to identify, anticipate, and transparently discuss ethical issues.

This study addresses the persistent gap between systematic literature reviews (SLRs) in software engineering and their practical uptake in industry, often referred to as the evidence-to-practice translation gap. To bridge this divide, the work introduces the Evidence to Decision (EtD) framework—originally developed in health sciences—into software engineering for the first time. By convening expert panels to conduct structured evaluations of SLR evidence against multidimensional criteria, the approach generates practitioner-oriented evidence briefs and actionable recommendations. This methodology strengthens the mechanism for translating research findings into real-world decisions, offering the first application of EtD in software engineering, identifying key dimensions essential for generating trustworthy recommendations, and highlighting major challenges that must be addressed for broader adoption of the framework.

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 persistent challenge of translating academic frameworks into actionable practices during the pre-production phase of AAA game development, where theoretical models often falter due to misalignment with industrial constraints, production realities, and cross-functional collaboration demands. Through in-depth interviews with 15 AAA game UX leads and subsequent qualitative analysis, the research elucidates how design decisions integrate theory, experiential knowledge, and evidence-informed intuition to balance player needs, technical feasibility, and creative vision. The work proposes a flexible theoretical toolkit that operationalizes academic concepts into context-sensitive insights, systematizes tacit expertise, and adapts to dynamic development workflows. Key contributions include a shared language for cross-team alignment, reusable design systems, and adaptive strategies that offer a practical pathway to bridge the gap between academic research and industry practice.

This work addresses the challenge of quantifying the academic impact of commercial engineering software such as Ansys Granta, which is hindered by inconsistent citation practices and rapidly growing publication volumes. We propose the first reproducible, semi-automated framework that integrates DOI and citation parsing, expert annotation, and a relational database (Ansys Granta MI Enterprise) to transform heterogeneous usage evidence into a structured knowledge base. As of September 2025, the framework has compiled a multi-source literature repository comprising over 1,100 manually verified records, enabling rapid retrieval, systematic review reproduction, and technology landscape scanning. The resulting knowledge base reveals dominant application domains, key contributing institutions, and integration patterns within CAD/CAE/FEM environments, thereby facilitating systematic tracking and analysis of the long-term technical influence of commercial engineering software.