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The increasing diversity and complexity of product configuration requirements in mass customization pose significant challenges for evaluating and advancing configuration technologies. Method: This paper introduces COOM Suite—the first structured, scalable benchmarking framework for product configuration—built upon the COOM modeling language. It comprises a hierarchical product model benchmark suite featuring three representative configuration fragment types: foundational, combinatorial, and constraint-intensive. A bicycle serves as an illustrative pedagogical example, complemented by extensible industrial-scale models. We propose a novel fragment-wise evaluation paradigm that jointly optimizes expressive power and solving efficiency, enabling seamless integration of multi-paradigm Answer Set Programming (ASP) solvers. Contribution/Results: The open-source COOM Suite significantly enhances modeling consistency, solving reproducibility, and industrial standardization. It provides a verifiable, comparable, and extensible benchmark infrastructure to rigorously assess and advance configuration technologies.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

In industrial cyber-physical systems (ICPS) research, demonstration objectives are often ill-defined, and technical feasibility assessment is frequently decoupled from outcome validation. Method: This paper proposes a five-level demonstration framework grounded in Maslow’s hierarchy of needs, systematically mapping demonstration goals to concrete research tasks and industrial use cases. It explicitly links work packages, verification metrics, and real-world scenarios, overcoming the vagueness and low operationality inherent in conventional Technology Readiness Level (TRL) frameworks. The approach integrates requirements engineering, modeling of software-intensive systems, and hierarchical framework design to support cross-phase requirements evolution analysis. Contribution/Results: Applied in two ICPS research projects, the framework effectively identified demonstration misalignments, refined requirement specifications, and significantly enhanced the precision, consistency, and rigor of feasibility assessment and project planning.

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.

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

This work addresses the lack of systematicity in engineering system design, often caused by ambiguous requirements and poor traceability, as well as the prevailing focus of existing AI tools on solution generation rather than problem formulation. To bridge this gap, we propose Design-OS—a lightweight, specification-driven five-stage design process that ensures end-to-end traceability from conceptual to parametric representations through structured design artifacts. For the first time, we extend specification-driven human-AI collaboration from software to physical system design, integrating control theory with systems engineering principles. The framework enables human-AI co-execution via autonomous agents within a unified, auditable, and hardware-agnostic workflow. We demonstrate its generality and reproducibility on two rotary inverted pendulum platforms, with open-sourced templates and complete design artifacts significantly enhancing transparency and systematic rigor.

This study addresses the insufficient test coverage for customer service AI agents in regulated industries and the risk that online experimentation poses to user trust. To overcome these challenges, this work proposes a hypothesis-driven simulation evaluation workflow. By leveraging the Snowglobe simulator and synthetic data generation techniques, the method enables multi-turn interaction simulation without invoking production backends. Furthermore, end-to-end binary evaluators are employed to pre-screen candidate agents for safety, facilitating large-scale configuration exploration with zero user risk. Experimental results demonstrate that simulated scores align closely with production environments. In A/B testing, the proposed approach yields a 36.69-point improvement in tNPS and an 8.82% increase in self-service rates, thereby achieving safe, iterative development of AI agents within regulated settings.

This work addresses the semantic gap between tactical Domain-Driven Design (DDD) patterns and general-purpose modeling languages, which often leads to persistent misalignment between design intent and code implementation. To bridge this gap, the authors propose a DDD-native metamodel that treats tactical DDD constructs as first-class modeling primitives and embeds expert architectural knowledge as executable constraints. Integrated with a real-time constraint validation engine and a bidirectional round-trip engineering mechanism, the approach ensures continuous consistency between models and code. By doing so, it substantially lowers the barrier to adopting tactical DDD, transforming it from an expert-dependent, elite practice into a tool-supported, widely reusable engineering methodology.

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 challenge of transforming heterogeneous IT service management (ITSM) ticket data into actionable, high-level decision intelligence. To this end, it proposes a sociotechnical AI pipeline that reframes ITSM analytics as a problem of transformation, abstraction, and human-centered design within information systems. The approach integrates large language model–driven schema normalization with HDBSCAN-based subtopic clustering and hierarchical agglomerative clustering to generate multilevel decision-support outputs—spanning both broad themes and fine-grained subtopics. Evaluated across six deliverables by five expert assessors, the resulting artifacts achieved mean scores above 4.0 (on a 5-point scale) for interpretability, actionability, trustworthiness, and willingness to use, with trustworthiness demonstrating particularly robust performance, thereby validating the method’s effectiveness and practical utility.