management

This study addresses the persistent paradox of engineering managers in agile software development: despite agile’s emphasis on decentralization and team autonomy, the engineering manager role remains salient. Adopting a mixed-methods approach—integrating systematic literature review with multi-case empirical investigation—the research identifies three interlocking drivers of this persistence: historical path dependence, theoretical tensions (e.g., balancing autonomy with cross-team coordination), and organizational constraints (e.g., technical debt governance, inter-team alignment, and talent development). Based on these findings, the paper proposes the “adaptive leadership” conceptual model, positioning engineering managers not as control-oriented supervisors but as enablers and architects who focus on technical strategy alignment, capability co-development, and systemic resilience. The model offers a theoretically grounded framework for rethinking leadership in agile organizations, informing management tool design and guiding future empirical inquiry. (149 words)

This study addresses the growing misalignment between conventional product management frameworks and agentic AI—systems characterized by autonomy, goal-directed behavior, and multi-agent collaboration. Drawing on systems theory, coevolution theory, and human–AI interaction theory, we conduct an integrative analysis of over 70 scholarly works and industry case studies from leading technology firms to develop the first holistic “human–AI coevolution” framework spanning the entire product lifecycle (from discovery to launch). The framework explicates bidirectional adaptation mechanisms between product managers and AI agents. It reconceptualizes the product manager as a coordinator of sociotechnical ecosystems, delineating novel responsibilities in AI collaboration, oversight, and strategic alignment. We identify three core competencies: AI literacy, governance capability, and systems thinking. This work bridges critical theoretical gaps in AI-augmented governance, role evolution, and dynamic integration, offering both foundational theory and actionable pathways for organizations pursuing responsible, high-efficiency human–AI collaboration.

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.

Traditional software project management struggles to address the emerging paradigm of AI-driven Software Engineering 3.0, particularly in human-AI collaboration and the establishment of ethical and accountability mechanisms. This work proposes an “agentive project management” framework, introducing the novel concept of an “agentive project manager” and designing four autonomous operational modes that dynamically balance automation and human oversight. It redefines the human role as a strategic leader and AI agent coach. Built upon a multi-agent system architecture, the framework integrates tunable autonomy, autonomous decision-making, and human-agent collaboration to enable dynamic task adaptation and traceable accountability. This study lays a theoretical foundation for project management in the SE 3.0 era and outlines a systematic research roadmap to advance paradigm evolution and community development.

Technical startups often suffer from inefficient software development due to weak project management capabilities. Method: This paper proposes a lightweight development process and a visual project management framework that integrates systematic innovation methods—specifically TRIZ tools (contradiction analysis and functional modeling)—with agile practices (Scrum/Kanban), establishing a closed-loop workflow spanning idea generation, validation, and implementation. It introduces a low-threshold task board and a dynamic requirement evolution tracking mechanism. Contribution/Results: The framework significantly reduces managerial cognitive load. Empirical evaluation demonstrates a 32% reduction in requirement delivery cycle time and a 1.9× improvement in cross-functional collaboration responsiveness. Designed for resource-constrained startups, the solution is scalable, easily implementable, and bridges the gap between innovation methodology and agile execution in early-stage software engineering.

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 study addresses the challenge of integrating strategic planning into agile development without compromising its empirical control and responsiveness. To this end, the authors propose the Milestone-Driven Agile Execution (MDAX) framework, which aligns project execution with organizational objectives by using strategic milestones to guide backlog prioritization. MDAX decouples high-level strategic planning from low-level implementation through a methodology-agnostic and mechanism-decoupled design, enabling organizations to flexibly adopt development practices best suited to their context. The framework enhances strategic alignment of project delivery while preserving the core agility needed for rapid adaptation. As such, MDAX offers an innovative and scalable solution for hybrid project management that effectively bridges strategic intent and agile execution.

This study addresses the empirical gap in understanding the application of large language models (LLMs) within Scrum management activities, particularly regarding their benefits and risks in non-technical agile practices. Through a survey of 70 Brazilian practitioners, it systematically characterizes the current adoption, usage frequency, and user proficiency of LLMs across Scrum artifacts and events. Findings reveal that 85% of respondents possess intermediate to advanced LLM competence, with 52% using them daily; 78% report enhanced efficiency and 75% note reduced manual effort. However, significant challenges persist: 81% encounter “almost correct” outputs, 63% express concerns about data confidentiality, and 59% are affected by hallucinations. The study quantifies both the tangible benefits and critical risks of LLM integration in Scrum, highlighting an uneven support landscape across its practices.

Although agile software development has been widely adopted, there remains a lack of a systematic, integrated framework identifying the critical success factors that consistently contribute to project success. Addressing this gap, this study conducts a systematic literature review of 53 empirical studies, employing thematic synthesis and content analysis to identify 21 critical success factors. These factors are systematically categorized into five dimensions—organizational, human, technical, process, and project—yielding a novel multidimensional theoretical framework. This framework represents the first comprehensive integration and classification of agile success factors, emphasizing the pivotal roles of team effectiveness and project management. It provides a solid theoretical foundation for future quantitative validation and practical application in agile contexts.

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.