standards interpretation

This work addresses the error-prone and labor-intensive process of manually translating regulatory texts such as the GDPR and the EU AI Act into actionable software requirements. The authors propose Reg2Req, the first end-to-end automated pipeline that leverages natural language processing to identify regulatory provisions, generate system-agnostic software requirements accompanied by plain-language explanations, and establish traceability links. The approach supports requirement classification, use case seed generation, and cross-reference analysis, achieving macro-averaged F1 scores of 0.82 on the GDPR and 0.78 on the EU AI Act. A user study demonstrates that the generated plain-language explanations significantly enhance users’ comprehension and confidence in taking compliance actions (p < 0.001), with all participants expressing willingness to adopt the output as a starting point for compliance efforts.

In safety-critical, regulated domains (e.g., aviation, healthcare), AI-driven design artifact generation tools suffer from poor explainability, leading to non-auditable decisions, high manual verification overhead, fragmented cross-role collaboration, and regulatory non-compliance risks. To address this, we conducted semi-structured interviews with domain experts and performed qualitative analysis to systematically identify explainability bottlenecks hindering the integration of NLP tools into requirements engineering. Building on these insights, we propose the first compliance-oriented explainability framework for such contexts, comprising four interlocking mechanisms: (1) decision provenance tracing, (2) explicit justification of generated artifacts, (3) domain-knowledge-aware adaptation support, and (4) automated regulatory compliance validation. Empirical evaluation demonstrates that the framework significantly enhances output transparency and trustworthiness, reduces manual review effort, strengthens stakeholder confidence, and improves collaborative efficiency—thereby offering both a practical implementation pathway and theoretical foundation for responsible AI deployment in high-assurance settings.

This study addresses the challenges posed by the proliferation, complexity, and expanding scope of regulatory requirements in software engineering, which hinder their systematic integration into development processes. To tackle this issue, the paper proposes a viewpoint-centered, artifact-based approach to regulatory requirements engineering. The approach innovatively integrates viewpoint analysis with artifact modeling to develop the AM4RRE (Artifact Modeling for Regulatory Requirements Engineering) framework, which facilitates cross-functional collaboration and ensures consistency in compliance-driven design. Preliminary validation demonstrates that AM4RRE effectively bridges the gap between organizational regulatory processes and software development practices, enabling a shift from ad hoc compliance responses toward systematic integration. This foundational work paves the way for further empirical investigation into scalable and sustainable regulatory compliance in software engineering.

Engineering design standards—typically expressed in natural language and tabular formats—are difficult for machines to interpret and validate automatically. Method: This paper proposes a modular ontology modeling approach grounded in the ISO/IEC/IEEE 24765 (IDO) top-level ontology, transforming textual and tabular specifications from standards such as ISO into OWL-based, W3C-compliant executable semantic ontologies, and integrating them with the ISO DIS 23726-3 Industrial Data Ontology. The resulting ontologies enable semantic reasoning and automated design rule verification. Results: The method achieves, for the first time, automated compliance checking against international materials and piping standards—including ASME B16.34 and ISO 15761—during valve selection. Its core contribution is a reusable, extensible semantic asset model that closes the loop from standard documents → machine-interpretable ontologies → design quality assurance, providing a practical, scalable pathway for standards development organizations to advance toward digital and intelligent transformation.

To address the inefficiency and error-proneness of manual regulatory compliance checking, this paper proposes an OWL DL formalization method for natural language specifications. The method introduces a novel structured text annotation scheme and employs a rule-driven deterministic transformation algorithm to automatically map specification texts to OWL DL ontologies. It further integrates Protégé with the HermiT reasoner to enable machine-readable semantic representation and automated compliance verification. A proof-of-concept evaluation in the construction domain demonstrates successful translation of multiple natural language regulations into OWL DL ontologies and accurate identification of compliant and non-compliant scenarios. This work bridges a critical gap between regulatory semantic modeling and automated reasoning, delivering a scalable, methodology-driven foundation for automating compliance checking.

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.

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 critical gap in evaluating large language models (LLMs) for industrial procurement scenarios, where compliance with Chinese national standards and safety constraints is essential yet overlooked by mainstream benchmarks. The work proposes the first evaluation framework that explicitly incorporates standard compliance and safety considerations, introducing a comprehensive benchmark spanning seven capability dimensions, ten industrial sectors, and multilingual aligned questions, alongside an independent safety violation detection mechanism. Leveraging retrieval-based question filtering, Qwen3-Max as an adjudicator (κ_w = 0.798), and a dual-track assessment of correctness and safety, the authors evaluate 17 Chinese and 8 cross-lingual models. Results reveal that even the top-performing system achieves only 2.083 out of 3, with safety violations substantially altering model rankings—highlighting the urgent need for source-aligned, safety-aware evaluation paradigms in industrial applications.

In AI-driven connected cyber-physical systems, annotation requirements suffer from ambiguity, misalignment among stakeholders, and unverifiability. Method: This paper proposes the first annotation-requirement–oriented dual-component framework: (1) Annotation Negotiation Cards—ensuring atomicity and stakeholder alignment; and (2) Scenario-based Annotation Specifications—enabling verifiability. Integrating requirements engineering, scenario modeling, collaborative requirement elicitation, and error-attribution mapping analysis, the framework is empirically validated in an autonomous driving perception case study. Results: It covers 18 real-world annotation errors, significantly improving annotation completeness, accuracy, and consistency. This work pioneers the systematic integration of annotation requirements into the requirements engineering discipline, establishing both theoretical foundations and practical tools for trustworthy AI data governance.

This study addresses the complex assurance challenges confronting AI-enabled Cyber-Physical Systems (AI-CPS) across perception, computation, control, human factors, and governance dimensions, noting that mere compliance with ISO/IEC 42001 fails to reveal architectural impacts or practical maturity. The authors propose CEDAR-42001, a two-stage method that uniquely maps compliance audit evidence onto a seven-layer AI-CPS architecture and governance hierarchy. By integrating a five-dimensional maturity profile, constraint identification, and rule-driven reasoning, the approach generates a traceable, architecture-aware assurance posture. Applied to an autonomous vehicle fleet case, it revealed that while 89.9% of audit items were compliant, only 34.3% met a high-assurance baseline. The method successfully reconstructed the 2023 Cruise incident, precisely identifying cross-layer deficiencies and recommending targeted mitigations to inform decision-making from strategic to operational levels.