platform engineering and prototyping

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

This paper addresses the challenges of weak consistency and high maintenance costs in software platform architecture evolution. We propose symmetry as a foundational architectural design principle: by identifying structural invariants—such as interfaces and behavioral specifications—under transformations (e.g., module replacement, deployment migration), we construct a formal architectural model incorporating symmetry constraints. Methodologically, we integrate group-theoretic modeling with structural analysis to quantitatively characterize the intrinsic relationship between architectural robustness and symmetry. Empirical and theoretical results demonstrate that symmetry constraints significantly improve system-wide consistency, reduce evolutionary complexity, and enhance scalability, reliability, and maintainability. To our knowledge, this work establishes the first systematic theoretical framework and formal modeling methodology for software architecture symmetry, offering a novel paradigm for designing resilient, evolvable platforms.

To address low testing and debugging efficiency and immature toolchains when deploying rapidly evolving large language models (LLMs) on emerging platforms (e.g., browsers, mobile devices), this paper proposes TapML—a top-down, test-driven framework. Methodologically, TapML introduces (1) the first operator-level test pruning technique that automatically generates high-coverage, realistic test inputs; (2) a progressive cross-platform migration strategy that significantly narrows the scope for compound error localization; and (3) native backend support for Metal and WebGPU, with deep integration into MLC-LLM. Evaluated over two years, TapML has enabled efficient deployment of 105 emerging models—spanning 27 distinct architectures—across five platform categories, reducing average deployment time by 42%. It has since become the default development paradigm for MLC-LLM.

To address integration challenges arising from the coexistence of heterogeneous platforms—such as AUTOSAR Adaptive and ROS 2—in Software-Defined Vehicles (SDVs), this paper proposes a hardware- and middleware-agnostic application development and integration methodology. The approach is grounded in platform-independent architecture design, leveraging standardized service interfaces and machine-readable metamodels (e.g., ASAM OSI-compliant descriptions) to enable semi-automated cross-platform integration. It further delivers a comprehensive toolchain supporting both Service-Oriented Architecture (SOA) and Software-as-a-Product (SaaP) paradigms. Experimental evaluation demonstrates significant improvements: interface adaptation time reduced by ~62%, cross-platform deployment cycle shortened by 55%, while ensuring functional consistency across platforms. This work provides a reusable methodology and practical foundation for evolving heterogeneous automotive E/E architectures toward service-oriented, integrated software ecosystems.

To address the challenges of heterogeneity, fragmented resources, and inefficient collaboration in embedded-system virtual-prototype simulation tools, this paper proposes SUNRISE—a scalable infrastructure for distributed simulation. SUNRISE introduces the Simulation Adapter Abstraction Layer (SAAL), a novel abstraction enabling plug-and-play integration of seven major commercial and open-source simulators. It leverages lightweight containerization (Docker/Kubernetes) and a RESTful microservice architecture to dynamically orchestrate simulation tasks across decentralized computing resources. An open API gateway is designed to facilitate cross-organizational collaboration. Experimental evaluation demonstrates that SUNRISE reduces simulation-task deployment latency by 62%, improves cross-organizational collaboration efficiency by 3×, and achieves a 99.8% API call success rate.

This study addresses the challenges of frequent model switching, energy efficiency, and reconfiguration overhead in autonomous driving scenarios by systematically comparing the deployment trade-offs between overlay architectures and custom accelerators. Through real workload-driven architectural simulations and multidimensional evaluation metrics—including reconfiguration latency, energy efficiency, and flexibility—the work provides the first quantitative assessment of these two architectural paradigms under realistic deployment conditions. The findings reveal that current overlay architectures are better suited for high-frequency switching scenarios; however, as reconfiguration overheads of custom accelerators decrease or the capabilities of overlay systems improve, the optimal deployment strategy may shift. These insights offer critical guidance for future heterogeneous hardware design targeting dynamic autonomous driving workloads.

This study addresses critical pain points in AUTOSAR Adaptive application development by revealing, for the first time from an industrial practice perspective, their root causes: inherent challenges arising from the interplay among the specification’s own architectural design and reuse objectives, vendor-specific implementation variations, and localized usage patterns. Employing a design science research methodology, the authors construct a minimal viable platform prototype and integrate configuration management analysis with runtime lifecycle modeling to systematically identify and attribute key issues. The primary contribution lies in demonstrating that design flaws at the specification level are the core catalysts of these challenges. The work further proposes optimizing the toolchain to reduce configuration complexity and training overhead, thereby offering empirical evidence and actionable pathways for improving the AUTOSAR Adaptive ecosystem.

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.

本文通过对比五个植物管理应用实现（iOS、Android原生及Flutter等跨平台框架），评估了软件质量的权衡，使用ISO/IEC 25010标准衡量不同维度性能。

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.