technical product management

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 paper addresses the Next Release Problem (NRP)—a multi-objective software requirements selection problem under resource constraints. We propose a scalable, generic optimization framework that uniformly models customer satisfaction, development cost, requirement attributes (e.g., priority, stability), inter-dependencies, and hard/soft constraints, enabling Pareto-optimal solution generation and stakeholder trade-off analysis. Our key contribution is the first formal, open-ended NRP modeling paradigm, designed to adaptively evolve with changing problem domains. Leveraging requirement dependency graphs, multi-objective optimization, and case-driven instantiation, we replicate and extend six existing solution approaches across six industrial case studies. Empirical results demonstrate the framework’s compatibility with diverse methodologies, high customizability, and practical effectiveness in real-world settings.

Existing research lacks systematic methods to assess how requirements engineering (RE) impacts downstream development activities, hindering RE process optimization. Method: This paper proposes the first fitness-for-purpose RE impact assessment model, integrating a systematic literature review with multi-source empirical data to identify and structure 24 downstream development activities affected by requirements and 16 quantifiable attributes. Contribution/Results: The model bridges two critical gaps in requirements quality assessment—namely, the “activity dimension” and “measurability of impact”—by enabling empirical analysis of how specific requirements artifacts and processes concretely influence development practices. It provides a theoretically grounded framework and evidence-based decision support for precise, targeted optimization of the RE phase.

In IT consulting, requirements specification writing faces challenges including fragmented domain knowledge and excessive time consumption. This paper proposes a human–AI collaborative requirements engineering paradigm: leveraging large language models (LLMs) as draft-generation engines, integrated with requirements summarization, template-guided structuring, and prompt engineering to automatically generate Epic-level Functional Design Specifications (FDS) and user stories. Human analysts focus on contextual understanding and technical validation, ensuring semantic accuracy and engineering feasibility. Experiments demonstrate that the approach reduces documentation time by 2.3× on average and cuts human effort by ~40%. Generated FDS documents achieve near-human performance in structural completeness and readability, with >92% coverage of critical requirements and manageable revision overhead. The core contribution is the first LLM-augmented requirements documentation framework tailored to consulting contexts—balancing automation efficiency with engineering reliability.

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.

This study addresses the challenge in axiomatic design of accurately translating customer needs and constraints into a minimal and independent set of primary functional requirements (FRs). Focusing on the problem definition phase, it systematically elucidates the nature, invariance, and formulation principles of primary FRs. Building upon Nam P. Suh’s theoretical framework and integrating insights from complexity theory and requirements engineering, the work establishes—for the first time—the objectivity and uniqueness of primary FRs, clarifies common misconceptions, and critically examines the applicability boundaries of large language models in this context. The research provides designers with a clear, actionable methodology for constructing primary FRs, thereby significantly enhancing the rigor of problem definition and the likelihood of successful design outcomes.

Existing approaches to requirement prioritization often overlook the semantic interdependencies among requirements, thereby compromising prioritization effectiveness. This work addresses this limitation by introducing requirement interconnectedness into user feedback–driven prioritization for the first time, proposing a dependency-aware search-based optimization framework. The method first applies natural language processing to cluster app store feedback into semantically coherent requirement groups and then automatically infers “requires”-type dependencies among these groups. These dependencies are explicitly integrated into a search algorithm to guide the optimization of requirement priorities. Evaluated on 94 real-world instances across four software systems, the proposed approach significantly outperforms ReFeed, demonstrating that explicitly modeling requirement interconnections effectively enhances both prioritization accuracy and release planning quality.

This work addresses the challenge of ensuring trustworthiness and stakeholder alignment in machine learning system development, which is often hindered by the absence of systematic requirements engineering. To bridge this gap, the authors propose REAL, a novel framework that uniquely integrates failure mode analysis into the requirements engineering process. REAL establishes a tripartite principle centered on data, model, and holistic system requirements, enabling iterative and traceable requirement refinement. Through a model-driven, stakeholder-oriented design, REAL demonstrates substantial improvements in requirement satisfaction in an autonomous driving case study. The authors further support reproducibility by releasing an open-source implementation toolkit.

In complex organizations, product diversity, legacy systems, organizational inertia, and regulatory constraints severely impede the adoption of end-to-end Continuous Software Engineering (CSE). Method: Drawing on empirical studies across automation, automotive, retail, and chemical industries, this paper proposes an evolutionary CSE adoption pathway. It extends the CSE readiness model by introducing explicit internal and external feedback layers and distinguishing market constraints (e.g., compliance requirements) from organizational constraints (e.g., process rigidity), thereby enabling phased, context-sensitive implementation. The model is validated and refined through expert interviews and narrative synthesis. Contribution/Results: Results demonstrate that—even without achieving full-chain continuous delivery—prioritizing internal engineering capability enhancement significantly improves delivery efficiency and business responsiveness. The extended readiness model supports pragmatic, incremental CSE adoption in highly regulated, heterogeneous environments.