assess feasibility

Engineering models (e.g., SysML) lack formal planning semantics—such as preconditions, effects, resource constraints, and temporal bounds—hindering task reachability and performance evaluation across system variants. Method: This paper proposes a model-driven approach natively integrated into SysML, leveraging a custom SysML profile to embed symbolic planning semantics directly into engineering models. It enables fully automated, bidirectional transformation from SysML models to PDDL domain and problem files—without external models or manual intervention—ensuring semantic consistency and model reusability. The method synergistically combines model transformation algorithms with symbolic planning techniques. Contribution/Results: Evaluated on an aircraft assembly case study, the approach validates functional feasibility and execution efficiency across multiple system variants. It significantly enhances interoperability between Model-Based Systems Engineering (MBSE) and AI planning, advancing automation and rigor in early-phase system design and analysis.

This work proposes a lightweight framework to address the high cost and poor contextual retention inherent in traditional engineering decision capture methods. By modeling decision alternatives as slices of system models and embedding them directly into model-based systems engineering (MBSE) workflows, the approach explicitly links decision knowledge with requirements, behavioral elements, and architectural components. This integration significantly reduces the overhead of decision documentation while enhancing the preservation of contextual information. The feasibility of the framework is demonstrated through a case study on aircraft architecture simplification, which confirms its effectiveness in improving decision reusability and integration efficiency within MBSE environments.

This study addresses the insufficient automation capability of Model-Driven Engineering (MDE) and the diminishing role of engineers in the AI era. Methodologically, it synthesizes metamodeling, AI-assisted modeling, model transformation, formal verification, and human factors engineering to establish a human–machine collaborative modeling paradigm, featuring multi-granularity model coordination and real-time feedback mechanisms. Key contributions include: (1) the first systematic identification of three fundamental bottlenecks—semantic gap, dynamic adaptability, and trustworthiness with explainability; (2) the distillation of six emerging engineering activities requiring automation across the full system lifecycle; and (3) the proposal of a theoretically grounded, industrially viable framework and research roadmap for intelligent MDE evolution, which balances automation efficacy with sustained engineer agency and domain expertise.

Conventional requirements engineering tools lack direct access to SysML architecture models, leading to redundant requirement definitions, semantic fragmentation, and broken traceability. Method: This paper proposes an executable, structured requirements metamodel that integrates INCOSE requirements writing practices with SysML modeling capabilities. Strictly aligned with ISO/IEC/IEEE 29148 and INCOSE guidelines, it leverages a SysML Profile extension, an MBSE integration framework, and a compliance rule engine to enable native interoperability between requirements and architecture models. Contribution/Results: The metamodel was deployed and validated on two real-world NASA JPL space systems. It significantly improves requirement semantic completeness and verifiability, enhances coverage of the NASA Systems Engineering Handbook checklist, and—critically—provides the first empirical evidence of rapid improvement in requirements expression quality. The evaluation also identifies key bottlenecks in current toolchains regarding automated support for such integrated practices.

Empirical evidence on the impact of Model-Driven Engineering (MDE) on software quality is fragmented and lacks systematic integration. Method: This paper conducts the first tertiary study dedicated to MDE quality research, systematically analyzing 22 published systematic literature reviews and mapping studies. It establishes a three-tier analytical framework to characterize research distribution, evidential strength, and methodological maturity in the MDE–quality domain. Results: Maintainability is the most studied quality attribute; however, among 83 identified research questions, 80 focus solely on conceptual or syntactic model-to-code mappings, with few conducting empirical comparisons. Crucially, MDE’s actual impact on quality in industrial development contexts remains markedly under-investigated. The study exposes a structural bias toward “re-modeling over validation” in current research and identifies critical gaps requiring urgent attention: rigorous experimental design, industry-based empirical validation, and multi-attribute quality assessment frameworks.

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 study addresses the limitations in automation and interoperability arising from tool heterogeneity in Model-Based Systems Engineering (MBSE) and Object Constraint Language (OCL) constraint validation, which often necessitate manual intervention. To overcome this challenge, the work proposes a unified verification framework that, for the first time, integrates the Asset Administration Shell (AAS) into the MBSE domain, combining AAS, OCL, and Model-Driven Architecture principles. This framework enables centralized management of constraints and their verification results while ensuring semantic consistency across tools. The approach significantly enhances the automation of model validation and improves interoperability among heterogeneous engineering tools. Its effectiveness is demonstrated through application in representative industrial scenarios. All artifacts have been open-sourced on GitHub to facilitate reproducibility and broader adoption.

This work addresses the challenge of reliably translating natural language into industrial-grade, deployable SysMLv2 models. The authors propose an iterative generate-check-repair framework that, for the first time, integrates a production-level SysMLv2 conformance checker directly into the generation process as a control mechanism rather than a post-processing step. By combining large language model (LLM) generation with deterministic diagnostic feedback and targeted repair strategies—and terminating only when zero errors remain—the method achieves perfect compliance. Evaluated across 604 test cases derived from 151 prompts and four distinct LLMs, the approach elevates single-pass generation compliance from 51.16% to 100%, enabling robust, direct translation of natural language specifications into engineering-ready SysMLv2 models.

This work addresses the lack of systematic methodologies in model optimization, which often relies on heuristic choices and struggles to accommodate diverse deployment constraints. It formalizes model compression and acceleration as a constraint-aware multi-objective engineering decision problem, establishing a unified and actionable framework grounded in five key dimensions: data availability, latency, memory footprint, accuracy tolerance, and retraining budget. By integrating techniques such as quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference optimization, the study proposes tailored optimization pipelines for four representative industrial scenarios, delivering a reproducible and quantifiable guide for technology selection.

This work addresses a critical limitation of traditional program repair approaches, which focus solely on code while overlooking the possibility that requirements themselves may be erroneous or outdated, leading to inconsistencies between system implementation and intended specifications. To bridge this gap, the paper introduces the first automated requirement repair framework tailored for Simulink Requirements Tables, shifting the repair target from code to requirements. The framework analyzes system execution traces, handles real-valued temporal signals, evaluates the semantics of declarative requirements, and automatically generates corrective patches. Evaluated across six real-world case studies involving twelve requirements, seven variants of the framework successfully produced correct and meaningful repairs, effectively restoring compliance between requirements and system behavior and addressing a key research gap in the co-evolution of requirements and implementations.