technical feasibility assessment

In model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

Existing search-based software testing (SBST) methods for Simulink models struggle to directly support natural-language Requirements Tables (RTs), necessitating cumbersome manual formalization of requirements into logical constraints. Method: This paper proposes the first black-box SBST framework natively driven by RTs. It introduces a semantic parsing and constraint mapping mechanism that automatically translates natural-language requirements in RTs into executable test constraints, integrated with genetic algorithms and Simulink’s simulation interface for end-to-end automated test generation—bypassing explicit logical formula translation. Contribution/Results: Evaluated on 60 real-world model–RT pairs, the approach achieves a 70% failure-revealing test case generation rate, matching the efficiency of state-of-the-art non-RT-based SBST tools. Moreover, it uncovered three critical failures in a cruise control model missed by other tools, demonstrating both industrial applicability and technical novelty.

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.

Safety requirements generated via STPA lack structured management and dynamic prioritization mechanisms. Method: This paper proposes an extensible closed-loop framework that integrates outputs across all STPA phases with multi-expert scoring, employs Monte Carlo simulation to quantify uncertainty and mitigate subjective bias, and enables robust requirement prioritization. Automated toolchain integration and a visual traceability matrix support end-to-end lifecycle tracking and decision-making—from conceptual design through high-level development. Contribution/Results: The framework is empirically validated in an eVTOL operations case study and has been formally adopted into the UK aviation regulatory document CAP3141. It significantly enhances efficiency, traceability, and regulatory compliance in safety requirement identification for emerging aviation systems.

Safety-critical small Unmanned Aircraft Systems (sUAS) lack systematic, standardized testing processes that are tightly integrated with safety analysis. Method: This paper proposes a requirement-driven coupled testing framework, introducing the novel triadic paradigm of “requirements–simulation testing–safety analysis.” It employs formal requirement modeling with bidirectional traceability, a simulation–hardware-in-the-loop cooperative testing architecture, scenario-driven test case generation, and deep integration of safety analysis methods (e.g., Fault Tree Analysis and System-Theoretic Process Analysis). Contribution/Results: Evaluated on an sUAS case study, the framework significantly improves simulation fidelity coverage and requirement coverage, enables end-to-end safety evidence generation, fills the gap in standardized sUAS testing procedures, and delivers reproducible, verifiable testing assets to support airworthiness certification.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

This work addresses the lack of standardized governance in AI-assisted development of scientific software, which currently hinders compliance with stringent quality assurance requirements such as ASME NQA-1. The authors propose a structured framework that, for the first time, integrates large language models with software verification and validation (V&V) methodologies under NQA-1 compliance. Using the open-source nuclear-grade code TMAP8 as a testbed, they establish an AI-assisted V&V use case development process that is verifiable, traceable, and auditable. The framework explicitly defines mechanisms for disclosing AI-generated content, subjecting it to rigorous review, and assigning human accountability, thereby unifying transparency, reproducibility, and regulatory compliance. This approach ensures software correctness while meeting the rigorous demands of high-assurance quality standards.

This study addresses the inefficiencies and impeded knowledge transfer arising from fragmented verification and validation (V&V) practices at the Jet Propulsion Laboratory (JPL). To overcome these challenges, this work proposes a unified V&V architecture grounded in human-centered design. By decoupling methodologies while maintaining a common attribute set, the architecture achieves bidirectional traceability through relational design and platform-independent SysML modeling. Furthermore, it establishes a comprehensive toolchain by integrating the Jama platform, modular templates, and digital thread technologies. This research effectively balances engineering rigor with agility, facilitating process automation, pattern reuse, and efficient cross-project collaboration. Ultimately, it provides a scalable and unified paradigm for the V&V of complex systems.

This study addresses the challenges of tool coordination and information consistency arising from downstream constraint backtracking in the multidisciplinary design of liquid oxygen/kerosene rocket thrust chambers. To this end, a long-range engineering agent framework is proposed. This framework employs a single coding agent to orchestrate performance, optimization, geometric, and multiphysics capabilities. It integrates a provenance-aware knowledge graph to assist method selection, constructs a typed intermediate representation to maintain shared parameters, and establishes revision-aware mechanisms for result invalidation and input blocking. Experimental results demonstrate that the proposed approach successfully handles operating point corrections following infeasible cooling searches, thereby validating both its sustained coordination capability across disciplinary workflows and the effectiveness of the dependency invalidation mechanism.

This work addresses a critical gap in the evaluation of software engineering agents, which has predominantly focused on code implementation while neglecting their ability to detect and correct defects in requirements specifications—such as omissions, ambiguities, and inconsistencies. We propose the first evaluation framework centered on specification-level reasoning, constructing a benchmark based on the RFC (Request for Comments) processes of open-source projects. The framework requires agents to systematically identify design flaws by synthesizing initial proposals, code repositories, and historical discussions. Evaluations across five repositories, including Kubernetes and React, reveal that even the best-performing model (GPT-5.4) achieves only 44.4% accuracy, highlighting a significant limitation in current agents’ capacity for requirement analysis and design review without execution feedback. This study thus fills a crucial void in assessing agent capabilities at the specification stage.