product operations

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.

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.

The increasing diversity and complexity of product configuration requirements in mass customization pose significant challenges for evaluating and advancing configuration technologies. Method: This paper introduces COOM Suite—the first structured, scalable benchmarking framework for product configuration—built upon the COOM modeling language. It comprises a hierarchical product model benchmark suite featuring three representative configuration fragment types: foundational, combinatorial, and constraint-intensive. A bicycle serves as an illustrative pedagogical example, complemented by extensible industrial-scale models. We propose a novel fragment-wise evaluation paradigm that jointly optimizes expressive power and solving efficiency, enabling seamless integration of multi-paradigm Answer Set Programming (ASP) solvers. Contribution/Results: The open-source COOM Suite significantly enhances modeling consistency, solving reproducibility, and industrial standardization. It provides a verifiable, comparable, and extensible benchmark infrastructure to rigorously assess and advance configuration technologies.

This study addresses the persistent challenge organizations face in aligning DevOps automation initiatives with strategic objectives such as waste reduction, delivery predictability, cross-team collaboration, and customer-perceived quality. To bridge this gap, the authors propose a unified VSM–GQM–DevOps framework that integrates Value Stream Mapping (VSM), the Goal-Question-Metric (GQM) approach, and DevOps practices. The framework enables identification of delivery bottlenecks, construction of decision-oriented measurement models, and implementation of maturity-aligned, reversible automation interventions, thereby establishing an auditable and traceable pathway for automation investment. Validated through a multi-site longitudinal study employing DORA metrics, interrupted time series analysis, and mixed-methods evaluation, the framework demonstrates significant improvements in delivery performance and project management outcomes, fostering continuous, strategy-aligned improvement.

Automating the generation of user story sets for new systems in software product lines—based on existing system families’ variability logic—remains challenging. Method: This paper proposes a synergistic approach integrating Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompt engineering. We pioneer the application of TCA to model the three-dimensional variability structure of “system–role–feature,” which guides LLMs to generate interpretable, semantically enriched, variability-aware user stories. Compliance, completeness, and consistency are ensured via option-guided prompting and multi-round validation. Results: Evaluated on a real-world dataset of 67 websites’ user stories, our method significantly improves requirement coverage (+23.6%) and cross-system consistency, while enabling traceable design decisions.

研究探讨了提高单次决策准确性是否能提升自主工作流执行成功率，通过对比分析不同模型在客户支持多轮对话中的表现，发现单次决策改进并不保证整体流程成功。

This work addresses the limited goal-directed execution capability of large language models in long-horizon tasks by introducing a Goal-Directed Execution (GDE) behavioral framework. The authors conduct post-training on the Qwen3.5-122B-A10B model using 363 long-horizon, multi-tool agent tasks from office scenarios, without relying on software engineering data. This approach yields a notable improvement on SWE-Bench Pro, increasing pass@1 by 5.8 percentage points. Experimental results demonstrate significant enhancements across four core GDE capabilities: goal selection, state construction, goal consistency maintenance, and environment validation. Furthermore, the model exhibits effective cross-domain transfer between office and software engineering tasks, confirming that long-horizon post-training can successfully drive the transfer of behavioral mechanisms.

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

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.