model deployment & operations

In the context of open science, there is an urgent need to implement the FAIR principles (Findable, Accessible, Interoperable, Reusable) in scientific data management, yet systematic guidance on achieving FAIR compliance through big data software reference architectures (SRAs) remains lacking. Method: We conducted a rigorous systematic literature review, screening 323 publications—including those from authoritative databases and expert recommendations—and performed structured data extraction and evaluation aligned with predefined research questions. Contribution/Results: The study identifies seven generic FAIR-compliant SRAs, thirteen scenario-specific FAIR pipelines, and three fully FAIR-compatible SRAs. It uncovers critical bottlenecks in metadata standardization, cross-platform interoperability, and long-term reusability. Furthermore, it establishes the first classification framework and empirical evaluation system for FAIR-oriented big data SRAs, thereby filling a significant research gap and providing a methodological foundation and strategic direction for future SRA design, policy formulation, and tool development.

This work addresses the challenge of silent updates to large language models (LLMs) by service providers, which often occur without version changes and can lead to behavioral drift and functional regressions, while existing mechanisms lack deployment-side control over compatibility governance. Framing LLM updates as a software supply chain governance problem, this study proposes a deployment-side control framework that defines rule-based production contracts, constructs risk-category-oriented test suites, and enforces compatibility gates to validate model safety and performance prior to updates. Experimental results demonstrate that the approach effectively uncovers fine-grained regressions missed by aggregate metrics, while also highlighting critical challenges in test design, threshold calibration, and drift attribution.

This study empirically characterizes, for the first time, the full lifecycle practices of pre-trained models (PTMs) in open-source software (OSS), focusing on integration, evolution, testing, and maintenance challenges. We conduct large-scale mining of GitHub repositories, coupled with cross-platform PTM dependency tracing (Hugging Face, PyTorch Hub), historical commit and issue log analysis, and model metadata parsing. Our analysis systematically identifies recurring risks—including dependency staleness, inadequate documentation, and insufficient test coverage. We propose a novel software engineering analysis framework specifically designed for model dependencies, addressing critical gaps in PTM operationalization and sustainability research. As concrete outcomes, we deliver a reusable PTM maintenance practice guide and a prototype detection tool. These contributions provide both theoretical foundations and practical support for enhancing the maintainability and engineering rigor of AI models within software systems.

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.

To address interdisciplinary interoperability, variant configuration governance, end-to-end traceability, and cross-organizational collaboration challenges arising from the networked evolution of Systems of Systems (SoS), this paper proposes a lifecycle management framework for Network-Centric Development (NCD). Methodologically, it grounds the framework in Model-Based Systems Engineering (MBSE) semantics and integrates Product Lifecycle Management (PLM) governance, CAD-CAE model synchronization, and closed-loop digital thread/digital twin capabilities. Its core contributions are four foundational principles: (1) reference architecture with a unified data model; (2) end-to-end configuration sovereignty; (3) review-driven model gating; and (4) quantifiable value contribution assessment. Empirical validation across transportation, healthcare, and public-sector domains demonstrates significant improvements in change robustness and model reuse rate, reduced delivery cycles, and enhanced support for sustainability-oriented decision-making.

This study bridges the gap between foundational model (FM) academic research and industrial practice by systematically investigating bidirectional interactions: FM-for-software-engineering (FM4SE) and software-engineering-for-FM (SE4FM). Method: We conduct a gray literature analysis of 1,152 technical blog posts from industry, introducing the novel “Model Jury” framework—integrating multi-LLM collaborative annotation, prompt-engineering-driven semantic classification, and abstractive summarization for automated large-scale analysis of unstructured text. Results: We find code generation dominates FM4SE applications; SE4FM primarily targets deployment, operations, and system orchestration; and edge-aware lightweight FM deployment is an emerging trend. We propose eight interdisciplinary research directions and open-source our dataset, prompt templates, and analysis code—establishing the first empirical benchmark and methodological foundation for FM4SE/SE4FM research.

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 lack of a scalable, traceable, and systematic approach to modernizing large-scale legacy systems while preserving both functional and non-functional characteristics. The authors propose a four-phase model-driven method that leverages a semantically rich intermediate model to uniformly abstract a legacy system’s structure, dependencies, and metadata. By designing semantics-preserving transformation rules, the approach enables semi-automated migration to modern platforms such as web-based architectures. The method establishes an end-to-end model-driven pipeline that integrates semantic metadata modeling with automated code synthesis. Evaluated on an industrial-scale .NET system, it successfully migrated core UI components, significantly enhancing maintainability and scalability while reducing modernization risks and manual effort.

To address challenges in Cyber-Physical Systems (CPS) development—including heterogeneous formal models, fragmented storage of modeling artifacts, inadequate version management, and limited knowledge reuse—this paper proposes an ontology-driven engineering knowledge graph framework. It introduces a unified systems engineering ontology built upon the custom Ontology Modelling Language (OML), enabling semantic integration of modeling artifacts across formal methods (e.g., SysML, UML, Modelica). The framework integrates a workflow engine, SPARQL querying, SWRL rule-based reasoning, and versioned graph storage to implicitly encapsulate complex knowledge graph operations. It is the first to support full-lifecycle semantic interoperability and automated knowledge discovery. Evaluated on an electric-drive intelligent sensor system, the framework significantly improves model version management efficiency, accelerates information retrieval, and uncovers three categories of latent engineering knowledge via inference.

In marine-domain multi-task anomaly detection, existing MLOps systems suffer from low reusability and high maintenance overhead. Method: This paper proposes a Ports-and-Adapters architectural approach tailored for Machine Learning-Enhanced Systems (MLES), deeply integrating the Hexagonal Architecture into MLOps practice. It decouples core business logic—including feature engineering and model inference—from external dependencies such as data sources, deployment environments, and monitoring services, thereby enabling highly cohesive, loosely coupled component reuse across microservices. The design supports flexible derivation of multiple domain-specific microservices from a single codebase and seamlessly integrates with ML pipelines and Domain-Driven Design. Contribution/Results: Evaluated in the Ocean Guard system, the architecture significantly improves code reuse, system portability, and team development velocity, while reducing deployment and iterative development complexity for marine AI systems across heterogeneous environments.