standards development

This study addresses the accelerating fragmentation of global AI governance, which exacerbates the divergence between technological development and regulatory frameworks, thereby impeding systemic interoperability and cross-jurisdictional compliance. To tackle this challenge, the work proposes a “technology–regulation dual interoperability” framework, systematically analyzing the root causes and interaction mechanisms underlying governance fragmentation through policy analysis, cross-jurisdictional comparison, and evaluation of standardization systems. The research reveals a trend of rapid yet dispersed growth in AI governance initiatives, identifies critical barriers to coordination, and offers strategic pathways and practical recommendations for fostering a compatible and coherent global AI governance ecosystem.

Current RESTful API design quality assessment relies heavily on manual inspection, lacking early, automated validation mechanisms for non-functional requirements—particularly interoperability, modularity, and maintainability. Method: This paper proposes an OpenAPI-based static analysis approach that implements a configurable rule engine. It formalizes 75 design principles derived from scholarly literature and industry standards into structured, machine-checkable constraints, enabling customizable rule activation/deactivation and traceable feedback to align requirements engineering with architectural governance. Contribution/Results: Following the design science research paradigm, we developed and evaluated a prototype tool. Empirical evaluation and expert review demonstrate that the method significantly improves API design compliance and consistency, achieving 82% automation coverage. It effectively supports continuous architectural governance in agile development environments, bridging the gap between design-time assurance and operational API lifecycle management.

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 study investigates design challenges of research infrastructure software—exemplified by the HERMES system—in multi-stakeholder environments, particularly under automated software release workflows, revealing significant misalignments between Research Software Engineers (RSEs) and Infrastructure Staff (IFs) regarding technical compatibility, usability, documentation quality, accountability mechanisms, and quality assurance. Method: A two-round structured survey and cross-group comparative analysis were conducted, introducing a novel hybrid analytical framework integrating organizational maturity assessment and usability evaluation to systematically identify inter-role priority differences and intra-group heterogeneity (e.g., technical experience gradients). Contribution/Results: Findings indicate IFs prioritize usability and governance assurance, whereas RSEs emphasize infrastructure compatibility; only 50% of RSEs actively perform software releases, hindered by both cultural inertia and technical barriers. The study provides empirically grounded insights and a methodological foundation for designing research software tailored to diverse stakeholder needs.

The European energy system’s transition toward renewable integration, digitalization, and distributed architectures necessitates enhanced cross-component and cross-system interoperability; however, a structured, comprehensive understanding of current interoperability testing capabilities across Europe remains absent. Method: This study conducts the first systematic survey of 30 European smart grid test facilities, employing a structured questionnaire and multidimensional comparative analysis, integrated with established standards, reference use-case libraries, and evaluation models to develop a taxonomy covering test environments, methodologies, and standardized use cases. Contribution/Results: The work delivers (i) the first pan-European interoperability test facility landscape map; (ii) a unified interoperability testing framework with an evolutionary roadmap; and (iii) publicly released classification criteria and infrastructure development guidelines. By addressing a critical systemic gap, this research enables transnational collaborative testing ecosystems and informs strategic decision-making for integrated, interoperable energy infrastructure.

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 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.

Current software engineering research is constrained by the large scale and closed-source nature of core industrial systems, as well as the opacity of real-world deployment environments, which hinder the reproducibility and in-depth investigation of practical challenges. This work systematically examines the evolution of research paradigms through a literature review and trend analysis, uncovering structural bottlenecks and advocating for transformative—rather than incremental—change. Its central contribution lies in proposing a novel research organizational model grounded in industrial PhD programs, sustained academia–industry collaboration, and large-scale research teams, complemented by aligned evaluation mechanisms. Together, these elements offer a strategic framework and actionable pathways to fundamentally reshape the software engineering research ecosystem.

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.

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.