product engineering

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.

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.

Inconsistent definitions of “feature” across software engineering domains—particularly requirements engineering (RE) and software product lines (SPL)—impede communication, trigger rework, and reduce cross-team collaboration efficiency. Method: We conducted an empirical study across 27 mainstream open-source projects, integrating repository mining, branch behavior analysis, qualitative coding, and pattern induction to derive a data-driven, cross-disciplinary definition of feature. Contribution/Results: This work introduces the first empirically grounded, unified feature definition framework bridging RE and SPL. It identifies recurring collaboration patterns and critical bottlenecks in feature description, implementation, and management, and proposes a roadmap linking academic theory with industrial practice. The findings yield actionable guidelines for project planning, resource allocation, and inter-team coordination, advancing feature conceptual standardization and engineering practice optimization.

This work addresses the state-space explosion problem in Petri net product lines, where concurrency and configuration variability are tightly coupled, by proposing a symbolic and parameterized approach to construct reachability graphs without exhaustively analyzing every product variant. The method employs a symbolic state encoding tailored to PNPL semantics and a successor-generation mechanism that preserves family-specific properties. It further integrates online merging of equivalent states and selective abstraction to achieve efficient state-space compression. By combining symbolic model checking, feature constraint propagation, and state equivalence checking, the approach significantly reduces both memory consumption and computational time while enabling reachability verification for product lines of realistic scale and retaining full diagnostic capabilities.

To address the lack of systematic continuous verification and secure release mechanisms in open-source hardware design, this paper pioneers the systematic adaptation of software CI/CD paradigms to the hardware domain, proposing a general-purpose framework for automatic hardware specification mining and continuous deployment. Methodologically, it integrates HDL static analysis, machine learning–driven specification inference, formal verification, and cloud-native automated pipelines, implemented in the prototype system Myrtha. Key contributions include: (1) the first CI/CD architecture supporting continuous hardware specification generation, verification, and release; (2) a scalable, automated specification mining mechanism that overcomes traditional manual modeling bottlenecks; and (3) substantial improvements in quality assurance, experimental reproducibility, and cross-team collaboration efficiency for open-source hardware development.

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 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 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 challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

This work addresses the challenge of balancing software quality, testability, and maintainability under rapid iteration and frequent requirement changes. It proposes Algorithm-Driven Development (ADD), a novel approach that unifies requirements specification and technical design by using algorithm flowcharts as a single, coherent artifact. This integration enables end-to-end modeling of requirements, architecture, and testing. Leveraging this model, the system automatically generates high-coverage acceptance tests and incorporates continuous integration with code coverage feedback. Industrial adoption at Dassault Systèmes demonstrates that ADD achieves over 95% code coverage, substantially reduces defect density, and ensures a stable delivery cadence, outperforming conventional test-driven development and test-after approaches.