architectural design

Sustainable system design requires balancing environmental resource protection, social equity, and economic viability—yet their interdependencies are complex and challenging to model formally. This paper proposes a metamodel-driven sustainable pattern engineering approach: it extends a sustainability metamodel to define a reusable pattern catalog covering resource conservation, circular economy, and global equity; introduces standardized pattern templates and a cross-dimensional (environmental–social–economic) co-modelling mechanism to support structured organization and cross-domain reuse. Grounded in metamodeling and case-driven methodology, the pattern catalog is instantiated in two real-world scenarios—fairness and circularity—demonstrating operational feasibility and cross-domain applicability. The core contribution is the first systematic formalization of sustainability strategies as a coherent, modelable, and reusable design pattern system, enabling formal, multi-dimensional expression and coordination of sustainability objectives.

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.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

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.

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 work addresses the semantic fragmentation and toolchain fragmentation in traditional model-driven engineering, which stem from the lack of a unified formal foundation among models, metamodels, templates, and transformations. To bridge this gap, the paper introduces Model Expression Algebra, treating models as values and expressions as terms, and unifying modeling operations through an evaluation homomorphism. By embedding a domain-specific language (DSL), the approach integrates metamodeling, model construction, and transformation within a single functional algebraic framework—unifying all four aspects for the first time. A type system ensures transformation safety, while free variables represent templates and computational operators encode functional logic, enabling type-preserving evaluation and built-in support for large-model expressions. Experimental results demonstrate that a single language can fulfill the full spectrum of modeling tasks while providing formal guarantees.

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.

This work proposes a native bounded structural model finding approach within the Maude rewriting logic framework, circumventing the reliance on external SAT/SMT solvers that hinders direct verification of software designs in their original formal setting. The method leverages symbolic reachability to automatically generate finite object configurations from class declarations and graph/data constraints, eliminating the need for custom generators. It introduces an obligation-driven bounded calculus that decouples object creation from reference assignment and integrates ACU matching for symmetry reduction. Correctness of folding is ensured via coverage-preserving entailment checks. By unifying symbolic rewriting, SMT-based pruning, and state exploration modulo equational theories, the approach guarantees termination, bounded completeness, and soundness. A prototype implementation demonstrates its effectiveness and expressiveness in generating structural models.

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 study addresses the limitations of traditional Design Structure Matrix (DSM) modularization approaches, which rely solely on graph-based optimization and lack engineering semantic context, often failing to align with practical design requirements. The authors propose a novel DSM modularization paradigm integrating large language models (LLMs), leveraging prompt engineering and iterative refinement to embed system-level semantic information directly into the partitioning process—achieving high-quality results without custom optimization code. Central to this work is the "semantic alignment hypothesis," which elucidates how improper incorporation of domain knowledge can degrade performance. Through systematic experiments across five representative engineering cases using three mainstream LLMs, the method demonstrates convergence to reference-quality modularization within 30 iterations, offering a reproducible and practical pathway for LLM-driven engineering design optimization.