customer engineering

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.

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 challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This work addresses the lack of systematicity in engineering system design, often caused by ambiguous requirements and poor traceability, as well as the prevailing focus of existing AI tools on solution generation rather than problem formulation. To bridge this gap, we propose Design-OS—a lightweight, specification-driven five-stage design process that ensures end-to-end traceability from conceptual to parametric representations through structured design artifacts. For the first time, we extend specification-driven human-AI collaboration from software to physical system design, integrating control theory with systems engineering principles. The framework enables human-AI co-execution via autonomous agents within a unified, auditable, and hardware-agnostic workflow. We demonstrate its generality and reproducibility on two rotary inverted pendulum platforms, with open-sourced templates and complete design artifacts significantly enhancing transparency and systematic rigor.

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 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 long-standing lack of systematic artificial intelligence support for critical decisions in product line engineering (PLE), such as feature selection, variability management, and configuration optimization. It proposes the first AI-integrated methodological framework specifically designed for PLE, which organically combines established product line engineering principles with advanced artificial intelligence techniques to enable intelligent decision-making across these core activities. Validated through multiple industrial case studies, the framework demonstrably enhances the automation and intelligence of product family development, offering a reusable and scalable pathway for AI-driven transformation in PLE.

Scalability in assurance case (AC) development and maintenance for software product lines (SPLs) remains challenging due to the need to simultaneously accommodate variant diversity, perform evolution impact analysis, and enable certification evidence reuse. Method: This paper proposes a variant-aware formal approach that elevates AC construction to the product-line level. We define a variant-aware AC language and a template-based construction mechanism, enabling unified modeling of safety evidence and supporting property-level scalability and sustainable certification. Integrating variant logic, formal modeling, and model-driven engineering, we develop an automated toolchain for AC generation and maintenance. Contribution/Results: Empirical evaluation on a medical device SPL demonstrates that our approach significantly improves traceability accuracy and evidence reuse efficiency under evolutionary changes, thereby advancing scalable, maintainable, and certifiable SPL assurance.

This study addresses the “Analytical Translation Problem” (ATP)—the lack of systematic support in translating business problems into technical machine learning solutions—by formally defining ATP for the first time. Through a structured narrative literature review, the authors classify and multidimensionally compare 18 existing methods from requirements engineering and ML project management. They develop a comprehensive analytical framework comprising four method families, six input–output categories, and seven transformation stages, revealing that most approaches offer little to no systematic guidance for deriving ML tasks and algorithms (only four provide limited support). Based on these insights, the paper proposes five novel recommendations: multi-solution exploration, task derivation guidance, constraint-to-algorithm filtering, probabilistic traceability, and data-triggered revision, thereby establishing a theoretical foundation and actionable pathways to bridge the gap between business needs and ML practice.

This study investigates software development teams’ awareness, attitudes, and readiness for organizational change prior to migrating to Software Product Line (SPL) engineering in Small and Medium-sized Enterprises (SMEs). Using semi-structured, in-depth interviews with key stakeholders across multiple roles, we conducted a qualitative study grounded in an SPL implementation framework and applied thematic analysis. Results indicate unanimous recognition of the migration’s strategic benefits, confirming the critical role of early stakeholder engagement in mitigating transition risks. Based on empirical findings, we propose a three-dimensional strategy to alleviate resistance to change: sustained cross-functional communication, incremental adoption of existing practices, and inclusive, collaborative implementation. This work addresses an empirical gap by systematically assessing pre-migration organizational cognition within SMEs—contextually distinct from large enterprises—and delivers actionable, context-sensitive change management guidance tailored for resource-constrained software organizations undertaking SPL adoption.