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Designs, implements, and operates the processes, roles, procedures, metrics, and supporting tools needed to deliver and continuously improve IT services across their lifecycle. This includes managing incident, problem, change, configuration, release and service‑level management, service catalog and service desk operations, capacity and availability planning, and vendor/SLA governance to achieve agreed service outcomes.
Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.
In large-scale compliance-driven IT projects, customized software processes often suffer from inflexible delivery and poor accessibility. To address this, this paper proposes the “Process-as-a-Service” (PaaS) paradigm—the first to abstract software processes as composable, subscribable cloud-native services. Leveraging domain-specific modeling (DSM) and a process metamodel for service encapsulation, the approach implements dynamic delivery via a RESTful microservice architecture and lightweight web-based interaction tools. It enables cross-project elastic adaptation and expert-guided real-time evolution. A proof-of-concept was conducted within a German public-sector software process line; evaluation by three domain experts confirmed significant improvements in process usability, user support efficiency, and compliance implementation convenience. The core contribution is the establishment of a novel, evolvable, and service-oriented software process paradigm tailored for regulated environments.
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.
本文提出了一种服务健康工程方法,通过结合遥测、工作流完成情况等手段来检测分布式系统中的静默故障和异步工作停滞问题。
Microservice system developers lack empirical evidence regarding the types, root causes, and remediation strategies of recurring issues. Method: We adopt a mixed-methods approach—quantitatively analyzing 2,641 open-source issues, qualitatively interviewing 15 practitioners, and conducting a global survey with 150 practitioners. Contribution/Results: We introduce the first comprehensive, domain-specific three-level taxonomy (“Issue–Cause–Solution”) for microservices. We identify five high-frequency issue domains—including technical debt, CI/CD pipeline failures, and exception handling—and three predominant root causes, notably generic programming errors. From our analysis, we distill 177 actionable, context-aware remediation strategies. This work establishes an empirical foundation for microservice fault diagnosis and mitigation, delivers practical guidance for industry practitioners, and pinpoints critical research directions for next-generation microservice engineering.
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.
为解决ERP系统中数据集成和流程监控的碎片化问题,本文提出一种企业流程控制塔,通过集成状态观测、语义翻译、机器学习诊断等方法提升IT团队的工作效率。
This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.
This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.
This work addresses the inadequacy of existing large language model (LLM) lifecycle frameworks, which predominantly emphasize operational efficiency while lacking explicit support for security-critical activities—such as data provenance, component signing, and access control—and failing to align governance requirements with specific lifecycle phases. The paper proposes the first security-oriented LLM system lifecycle model, structured not by workflow but by security boundaries, organizing 32 phases into four layered pipelines: data, model, distribution, and application, while integrating LLMOps and governance pillars. It uniquely identifies 13 distinct security-critical phases and exposes a structural imbalance wherein regulatory evidence is concentrated at deployment despite pivotal decisions occurring during development. By mapping key standards—including NIST AI RMF, the EU AI Act, and ISO/IEC 42001—the study establishes a phase-to-governance correspondence mechanism, yielding a comprehensive, lifecycle-spanning security analysis framework that offers structured guidance for compliance and secure design.