product management

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 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.

FinOps product innovation in cloud financial management faces persistent bottlenecks—including poor understanding of customer needs, low cross-functional collaboration, and suboptimal resource allocation. Method: This study pioneers the systematic integration of a User Experience Research Point of View (UXR PoV) into the product development lifecycle, establishing a mixed-methods research framework comprising in-depth interviews, contextual inquiry, surveys, behavioral analytics, and user segmentation modeling. Contribution/Results: By identifying core pain points, enabling granular user segmentation, and designing cross-team collaboration mechanisms, we propose a novel “one-stop” integrated FinOps dashboard paradigm. The resulting reusable UXR PoV framework supports closed-loop product decision-making. Empirical evaluation demonstrates a 37% increase in user task completion rate and a 52% improvement in resource priority alignment efficiency.

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.

This study addresses the inefficiencies in requirements management within large-scale agile development, stemming from the absence of a unified requirements engineering process and high-level guiding principles. Through a five-year longitudinal industrial case study encompassing over 25 sprints, more than 320 weekly meetings, seven cross-organizational workshops, and focused group interviews, the research employs thematic analysis to distill six transferable and scalable core principles—such as architectural context, stakeholder-driven validation, and lightweight documentation evolution. Validated across multiple multinational enterprises, these principles significantly enhance requirements management effectiveness in large-scale agile settings. This work presents the first systematic strategic requirements engineering framework tailored specifically for such complex environments.

This work proposes Visual Milestone Planning (VMP), a novel approach that addresses the lack of intuitive, collaborative milestone planning mechanisms in hybrid development environments where agile teams struggle to integrate with traditional planning paradigms. VMP innovatively combines a milestone planning matrix with a physically inspired visual scheduling mechanism: product backlog items are mapped to milestones and arranged as Tetris-like work packages on a resource–time canvas, enabling dynamic determination of milestone deadlines. By bridging agile practices with conventional project planning, the method significantly enhances team collaboration, planning transparency, and shared understanding of delivery cadence.

This study addresses the unclear practical impact of generative AI in requirements engineering (RE) within current industrial practice, particularly regarding tool integration, team collaboration, and organizational adaptability. Drawing on a company-wide use case survey conducted in 2024 and two rounds of interviews with eight product owners during 2025–2026, the research systematically analyzes fifteen RE use cases across four categories, leveraging an in-house chatbot and seven commercial generative AI tools. Findings reveal that AI adoption has moved beyond individual productivity gains to influence complex scenarios such as cross-tool integration, customer governance responses, and role boundary reconfiguration. The degree of tool integration critically determines performance benefits, while single-user interaction modes may undermine collaborative dynamics. The study proposes a practitioner-oriented set of evaluation questions to guide effective industrial deployment of AI in RE.

This study addresses the limitations of traditional B2B customer segmentation approaches, such as the RFM model, which rely on singular metrics and struggle to capture the complexity and dynamics of business interactions. To overcome this, the authors propose a dynamic, multi-criteria segmentation framework that extends RFM by incorporating stability and growth dimensions. The framework aligns with strategic business objectives through an adaptive Analytic Hierarchy Process (AHP) and integrates multivariate time series clustering with a graph consensus model to enable temporal segmentation. Evaluated on data from over 3,000 manufacturing enterprises, the approach demonstrates strong temporal robustness and significantly enhances the precision of customer strategy formulation through preference-driven dynamic clustering.