enterprise systems

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.

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.

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.

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.

This study addresses the limited understanding of how practitioners actually develop software engineering (SE) agents, particularly the lack of systematic investigation into the evolution of development workflows and core challenges. Through semi-structured interviews with 20 practitioners complemented by a survey of 80 respondents, this work proposes the first seven-stage workflow for SE agent development, revealing a paradigm shift toward “evaluation-driven iteration.” The research identifies that bottlenecks have moved beyond coding to non-coding tasks such as requirement specification, cross-role coordination, review, and deployment. It systematically characterizes six key challenges—including unreliable evaluation signals, accumulating comprehension debt, and behavioral drift induced by model updates—and synthesizes corresponding practical mitigation strategies.

本文探讨了通过整合数据工程和软件工程实践（如DataOps、MLOps等）来重塑面向数据和AI系统的软件开发生命周期，以应对传统SDLC在处理这些系统时遇到的挑战。

论文探讨了在软件代理时代，如何通过可信赖变更和责任拓扑结构来管理可扩展执行，以使组织能够接受并维持其变化。

Existing agent organizations are often oversimplified as static dialogue topologies or fixed workflows, struggling to balance persistence with dynamism. This work proposes a native agent organization architecture featuring four hierarchical layers—persistence, coordination, runtime, and human-agent interaction—that constructs task-specific operational worlds through permission/privilege mechanisms while orthogonally separating execution, auditing, and oversight roles. The design adopts a dual-state paradigm: rigid at the foundational layer yet flexible at the execution interface, ensuring persistent integrity of records, write constraints, and separation of powers while enabling task-driven dynamic reconfiguration. Key technical components include a four-store recording architecture, specialized agent pools, isolated runtimes, and non-decisional translator agents. A prototype implementation demonstrates the framework’s feasibility, offering a falsifiable foundation for the design, governance, and evaluation of agent organizations.

This study addresses the semantic, behavioral, and interoperability challenges inherent in cross-organizational SysML model integration. By surveying Model-Based Systems Engineering (MBSE) stakeholders through online questionnaires employing Likert scales, we evaluate the supportive utility of large language models (LLMs). Our findings reveal multidimensional alignment difficulties and identify high-value scenarios where AI excels in semantic analysis and inconsistency detection. Consequently, this work proposes an AI-assisted paradigm that augments rather than replaces engineering accountability. The core contribution lies in establishing a human-in-the-loop collaborative verification mechanism, providing both empirical evidence and a practical framework for AI-empowered complex systems engineering.