system architecture design

Current micro-frontend architectures heavily rely on specific bundlers (e.g., Webpack), leading to inflexible module composition, constrained cross-team collaboration, and bottlenecks in error detection, runtime observability, and loading performance. To address these limitations, we propose Bundler-Independent Module Federation (BIMF)—the first runtime module federation framework decoupled from build-time bundlers. BIMF enables dynamic module loading, type-safe inter-module collaboration, and cross-team dependency sharing. It integrates runtime dependency resolution, distributed tracing, server-side rendering (SSR), and intelligent prefetching to significantly enhance observability and first-contentful-paint (FCP) performance. Experimental evaluation of a prototype implementation demonstrates: (1) full preservation of TypeScript type contracts across modules; (2) 100% dependency deduplication; (3) a 37% reduction in average module loading latency; and (4) a 42% improvement in parallel development efficiency across distributed teams.

To address the challenge in software design where abstract models struggle to simultaneously achieve intuitiveness, integrability, and code translatability, this paper introduces Conceptual—a novel behavioral modeling domain-specific language (DSL) grounded in self-contained, highly reusable “concepts.” Methodologically, it formalizes the DSL’s semantics based on concepts, establishes a rigorous semantic mapping from Conceptual to Alloy to leverage Alloy’s formal verification capabilities, and implements a VS Code–based prototype toolchain supporting syntax highlighting, parsing, and model transformation. Contributions include: (1) the first formal semantics for a concept-based DSL; (2) a sound, executable translation to Alloy enabling automated consistency checking; and (3) an integrated development environment demonstrating practical usability. Empirical evaluation shows that Conceptual accurately captures design intent across diverse domains; its prototype compiler has successfully detected multiple specification errors reported in prior literature, thereby validating its expressive power, logical consistency, and engineering feasibility.

Existing modeling frameworks lack native support for multi-path design evolution—such as branching, revisioning, and merging—relying instead on external version-control and collaboration tools. This hinders traceability and collaborative efficiency in complex systems design. Method: We propose the “Design Multiverse” paradigm, the first approach to natively embed dynamic design operations—including branching, revisioning, and merging—within the modeling environment, enabling co-evolution of model product lines and model–metamodel relationships. Grounded in the Model Federation paradigm, our approach integrates multi-model coordination with fine-grained version control to unify the management of design state snapshots. Contribution/Results: The method significantly enhances collaborative efficiency among heterogeneous stakeholders and improves end-to-end decision traceability across the system lifecycle, particularly in large-scale, multi-domain design scenarios.

Existing software architecture frameworks inadequately model machine learning (ML) systems, as they overlook the needs of emerging stakeholders—such as data scientists and data engineers—and lack expressive support for ML-specific characteristics, including component uncertainty, heterogeneity, and collaborative behavior. Method: Through an empirical study involving interviews and surveys with 61 domain experts from 25 organizations across 10 countries, we systematically identified ML-relevant stakeholders and their concerns for the first time. Contribution/Results: We propose novel, ML-adapted architectural viewpoints and views, extending traditional frameworks to enable unified modeling of both ML and non-ML components. This yields the *ML-Enhanced Systems Architecture Framework Extension Guide*, which has been preliminarily adopted in industry for intelligent system architecture governance. Our work bridges a critical theoretical and practical gap in stakeholder modeling and viewpoint systematization for ML system architecture design.

The academic positioning of low-code development relative to classical model-driven development remains ambiguous, and the relationship between their respective research communities lacks systematic clarification. Method: This paper conducts the first meta-scientific study, integrating bibliometric analysis, author-venue-topic network modeling, and cross-community comparative analysis to quantitatively characterize the low-code community’s scale, disciplinary diversity, publication venue distribution, and scholarly output characteristics—and to systematically compare them with those of the classical model-driven development community. Contribution/Results: We find that the low-code community exhibits strong interdisciplinarity and conference-centric publication patterns, and has significantly diverged from traditional modeling communities. These findings provide empirical grounding for conceptualizing low-code as an independent research trajectory, reveal opportunities for disciplinary integration, and identify critical interfaces for collaborative innovation—thereby informing the reconfiguration and convergence of the broader modeling research community.

Microservices achieve physical isolation but fail to prevent the proliferation of logical coupling, undermining module independence. This paper proposes a novel modularization paradigm based on universal interface boundaries, constructs a quantifiable model for assessing module independence, and designs a runtime mechanism supporting dynamic loading, unloading, and hot updates within a single process. Its core contributions are: (1) reframing module independence as a formal, modelable, and measurable system property—moving beyond qualitative assertions; (2) replacing implicit dependencies with explicit interface contracts to fundamentally block coupling propagation; and (3) implementing the EIGHT platform prototype, which achieves microservice-level module autonomy within a monolithic process. Experimental results demonstrate that the approach significantly reduces the impact scope of cross-module changes, enhancing system maintainability and evolutionary efficiency. It provides both theoretical foundations and practical pathways for next-generation architectures transcending the monolith–microservice dichotomy.

Traditional AI systems rely on fixed monolithic models, which struggle to dynamically allocate resources, decompose tasks, or update knowledge in response to varying inputs, leading to degraded performance and increased costs. This work proposes the first system-level design methodology for distributed composite AI systems, formulating a design space through workflow topologies and configuration choices and identifying eight core design patterns. The framework jointly optimizes model selection and runtime parameters, enabling task decomposition, multi-model orchestration, and explicit control logic, thereby facilitating a shift from static monolithic architectures toward dynamic, composable, and adaptive ones. Evaluated across three case studies, the approach reduces latency by up to 60% and cost by up to 71%, with only a 2.5–4 percentage point drop in accuracy.

This study addresses the interoperability challenges in automotive domain modeling arising from the coexistence of heterogeneous tools, multiple modeling languages, and a mix of proprietary and open-source environments. To tackle this issue, the work proposes a novel automated approach that leverages large language models (LLMs) to map and merge source model instances into target metamodels based on Ecore and SysML v2. A structural validation mechanism is integrated to ensure semantic consistency and syntactic correctness of the generated models. Experimental evaluation on real-world automotive cases demonstrates that the method substantially reduces manual transformation effort while efficiently producing target models that are both structurally valid and aligned with user requirements, thereby establishing a viable new paradigm for cross-tool modeling interoperability.

Modeling complex concurrent and timing-sensitive systems faces challenges in multi-objective compilation (for simulation, deployment, and formal verification), weak semantic consistency across targets, and the lack of expressive, unified modeling languages. Method: This paper introduces M, a textual modeling language grounded in the Actor model and discrete-event scheduling semantics, supporting temporal/state-triggered behaviors and asynchronous message passing. We design the first reusable, multi-target model compilation framework that uses M as a unified intermediate representation to enable semantics-preserving model transformations and code generation across heterogeneous targets. Contribution/Results: M serves as a common anchor for diverse domain-specific modeling languages, significantly enhancing model reusability and toolchain interoperability. The framework provides a general-purpose compilation infrastructure for heterogeneous system development—bridging simulation, implementation, and formal verification—while ensuring end-to-end semantic fidelity across compilation targets.

This study addresses the prevailing gap in AI education, which emphasizes model development while neglecting system engineering practices, leaving students ill-equipped to handle real-world challenges such as architectural design, deployment, and monitoring. To bridge this gap, the authors implemented a master’s-level course in which students built a movie recommendation system under realistic constraints, with a focus on integrating AI components into robust software systems, adopting data-driven machine learning practices, and cultivating systems-level thinking. Using a mixed-methods approach—combining analysis of student project artifacts with survey data—the research evaluates learners’ performance in architectural decision-making, integration of heterogeneous models, and adaptation to evolving requirements. Findings reveal common difficulties students encounter in AI system engineering and demonstrate the course’s effectiveness in addressing critical deficiencies in AI engineering education and enhancing systems-aware competencies.