product development

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

This paper addresses the Next Release Problem (NRP)—a multi-objective software requirements selection problem under resource constraints. We propose a scalable, generic optimization framework that uniformly models customer satisfaction, development cost, requirement attributes (e.g., priority, stability), inter-dependencies, and hard/soft constraints, enabling Pareto-optimal solution generation and stakeholder trade-off analysis. Our key contribution is the first formal, open-ended NRP modeling paradigm, designed to adaptively evolve with changing problem domains. Leveraging requirement dependency graphs, multi-objective optimization, and case-driven instantiation, we replicate and extend six existing solution approaches across six industrial case studies. Empirical results demonstrate the framework’s compatibility with diverse methodologies, high customizability, and practical effectiveness in real-world settings.

Industrial Control and Automation Software (iCAS) suffers from ambiguous design concepts and a lack of foundational design theory, leading to empiricism and inconsistent engineering practices. Method: This study systematically introduces design theory to iCAS for the first time, establishing a scientifically grounded design-definition framework. Through interdisciplinary literature review and theoretical analysis, it clarifies the ontological boundaries, core semantics, and quality criteria of design; rigorously distinguishes “design activity” from “design language”; and proposes a novel design balance mechanism reconciling system evolvability with real-time operational constraints. Contribution/Results: The work yields a reusable design metatheory for iCAS software engineering, enhancing design process standardization, traceability, and innovation capacity. It advances the field from experience-driven practice toward theory-guided development, providing principled foundations for systematic design methodology and implementation pathways.

Existing research lacks systematic methods to assess how requirements engineering (RE) impacts downstream development activities, hindering RE process optimization. Method: This paper proposes the first fitness-for-purpose RE impact assessment model, integrating a systematic literature review with multi-source empirical data to identify and structure 24 downstream development activities affected by requirements and 16 quantifiable attributes. Contribution/Results: The model bridges two critical gaps in requirements quality assessment—namely, the “activity dimension” and “measurability of impact”—by enabling empirical analysis of how specific requirements artifacts and processes concretely influence development practices. It provides a theoretically grounded framework and evidence-based decision support for precise, targeted optimization of the RE phase.

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 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 investigates how goal-aligned and goal-agnostic reward mechanisms influence decision-making behavior and design diversity in creative tasks. Using a 3D parametric chair design task as the experimental setting, the design process is formalized as a Markov decision process, and a mixed-methods approach—integrating user behavior tracking, experimental psychology paradigms, and hybrid analytical techniques—is employed to systematically examine participants’ exploration strategies and subjective experiences under different reward conditions. The findings reveal that goal-aligned rewards not only enhance goal attainment but also foster more thorough exploration of the design space while preserving diversity. Moreover, the nature of the design goal significantly moderates users’ perceived usefulness of the rewards. These results elucidate the synergistic mechanism between rewards and goals and offer actionable guidelines for designing effective feedback systems in creative design contexts.

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.

This work addresses the problem of implementation drift in evolving distributed systems, where runtime behavior gradually deviates from the original design. To tackle this issue, the paper proposes a design conformance assessment method based on distributed tracing data. It introduces, for the first time in the domain of distributed systems, conformance checking techniques from process mining, leveraging runtime traces collected via the OpenTelemetry standard and automatically comparing them against behavioral models defined at design time to quantify their alignment. The key contribution lies in establishing persistent, monitorable conformance metrics that enable continuous, automated evaluation of deviations between system implementation and design. This approach is readily applicable to modern distributed systems widely adopting OpenTelemetry for observability.

This study addresses a critical limitation of existing DORA metrics, which rely solely on first-order statistics and thus fail to capture the distributional characteristics of software release cadence or distinguish teams with markedly different release regularity. To overcome this, the work introduces second-order statistics into the DORA framework for the first time, proposing a novel Delivery Consistency (DC) metric based on the coefficient of variation of inter-release intervals. It further constructs an eight-prototype Delivery Health Matrix to enable multidimensional diagnosis and targeted intervention for software delivery rhythms across platforms. Validation using real-world data spanning 120 weeks from four platforms—including Jira, GitHub, and Firebase—demonstrates that the approach effectively identifies teams sharing identical DORA ratings yet exhibiting divergent release patterns, uncovering underlying organizational or process constraints common to such teams.