system design

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.

Addressing the challenges of architectural design misalignment and empirically unsupported technology selection in large-scale, highly dynamic, multi-source heterogeneous data environments, this paper proposes a semi-automated data-intensive architecture design methodology. The approach comprises three foundational mechanisms: (1) a formal service-oriented application scenario description language; (2) an architecture description language (ADL) supporting mainstream paradigms—including stream processing, batch processing, and graph computation; and (3) a system taxonomy integrating functional and performance trade-offs. Leveraging formal modeling and case-driven validation, the methodology is evaluated across multiple real-world literature cases. Results demonstrate significant improvements in architecture customization accuracy and technology selection rationality, while simultaneously optimizing service quality and resource utilization efficiency.

Cloud architecture design faces challenges including ambiguous requirements, implicit decision-making processes, and complex interdependencies among architectural elements. To address these, this paper introduces CloudArchitectBuddy—a novel system featuring a synergistic dual-mechanism framework: *structured state management* and *guided decision support*. It explicitly models requirement evolution and design decisions via a finite-state machine, integrates rule-based validation with large language models (LLMs) for requirement refinement and consistency checking, and establishes a hybrid human–AI collaboration paradigm combining structured workflows with natural-language dialogue. Evaluated with 16 industry practitioners, CloudArchitectBuddy achieves design quality comparable to pure chat-based interfaces while significantly improving usability (*p* < 0.01), architectural relationship comprehension (+32%), and missing requirement identification (+41%). These results empirically validate the effectiveness and practical value of its mixed-interaction approach.

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.

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 lack of systematic methodologies for selecting data architectures in modern organizations grappling with vast, heterogeneous data environments. To this end, it proposes the DATER conceptual framework, which establishes a unified taxonomy of technical requirements and systematically examines the historical evolution, core characteristics, and applicability boundaries of six prominent data architectures: data warehouses, data lakes, lakehouses, data fabrics, and data meshes. Through conceptual modeling and multidimensional comparative analysis, the framework clarifies overlaps and distinctions among these architectures, articulating their respective strengths and limitations. By offering a structured evaluation tool, DATER significantly enhances the strategic alignment and contextual appropriateness of data architecture design for both researchers and practitioners.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This study addresses the proliferation of functional redundancy in service-oriented architectures caused by heterogeneous clients, which undermines system evolvability and maintainability. To mitigate this issue, the authors propose a novel reference architecture that synergistically integrates metadata-driven mechanisms with pattern languages. By leveraging metadata management and a plugin-based design, the approach effectively constrains service redundancy while enhancing reuse capabilities. The work innovatively combines metadata mechanisms and pattern languages in architectural construction and validates its efficacy through a triangulated evaluation method incorporating scenario-based assessment and real-world case studies. Empirical results demonstrate that the majority of system changes during evolution require no code modifications—only configuration adjustments or the addition of pluggable components—thereby significantly improving architectural stability and reuse efficiency.

This work addresses the challenge of effectively evaluating the trade-offs between data consistency and coordination overhead among distributed transaction patterns—such as Saga and TCC—in business logic-intensive microservice systems prior to production deployment. The authors propose a lightweight microservice simulator grounded in Domain-Driven Design (DDD), which, for the first time, integrates DDD aggregate root modeling with multiple transaction models to decouple business logic from communication and transactional infrastructure. The framework supports configurable deployment topologies and network constraints, enabling seamless transitions from centralized to fully distributed architectures while providing a deterministic verification environment. Empirical evaluation on complex multi-aggregate systems quantifies the performance, coordination overhead, and resilience of different transaction models, substantially reducing development costs and facilitating left-shifted architectural validation.

This work addresses the complexity and heavy reliance on manual effort in software architecture design, particularly the challenge of efficiently exploring diverse architectural solutions within agile development contexts. To this end, the paper proposes MAAD, a novel framework that systematically integrates multi-agent collaboration, Retrieval-Augmented Generation (RAG), and hierarchical memory mechanisms into the architectural design process. MAAD employs four specialized agents—analyst, modeler, designer, and evaluator—that collaboratively transform requirements into multi-view architectural blueprints and support iterative refinement. Experimental results across ten case studies demonstrate that MAAD-generated architectures consistently outperform those produced by the MetaGPT baseline in terms of completeness, modularity, and traceability. Furthermore, the evaluator agent automatically produces structured quality reports, substantially reducing the cost of manual validation.