leadership

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 work addresses the gap in current systems education, where learning resources often consist of superficial tutorials or AI-generated summaries that inadequately convey foundational design principles and thus fail to cultivate robust engineering capabilities. To remedy this, we propose a structured learning pathway centered on seminal research papers from distributed systems, operating systems, and big data domains. Integrating insights from leading academic curricula and industry practices, our approach emphasizes technical depth and problem-solving reasoning. By engaging learners in close reading of original literature, critical analysis of architectural trade-offs, and cross-domain synthesis, the framework fosters a deep understanding of underlying mechanisms and cultivates systems thinking—thereby equipping practitioners to effectively tackle complex engineering challenges and progress toward professional-level systems expertise.

This work addresses the widespread absence of structured career pathways for Research Software Engineers (RSEs) in higher education institutions, which has hindered effective recognition of their technical expertise, scholarly contributions, and leadership potential. The project introduces the first standardized yet flexible RSE career ladder within a university setting, spanning levels from Assistant to Principal and offering dual-track progression in both technical and managerial roles. By integrating a competency framework, external expert consultation, standardized job grading, and market-aligned salary benchmarking, the framework achieves a balanced alignment between institutional human resources policies and individual professional development needs. Implementation of this system has demonstrably enhanced recruitment efficiency, increased transparency in promotion processes, and garnered strong endorsement from the RSE community.

Contemporary technical hiring practices suffer from stress-induced bias and evidentiary gaps, resulting in distorted competency assessments and compromised fairness. Method: This paper proposes an evidence-driven paradigm for software engineer competency evaluation. It systematically identifies and bridges evidentiary gaps in technical hiring through (1) multi-source behavioral data integration, (2) low-stress, authentic task design, and (3) a verifiable fairness framework grounded in educational measurement, human-computer interaction evaluation, algorithmic fairness auditing, and structured competency modeling. Contribution/Results: The approach yields a scalable, empirically validated hiring effectiveness metric suite. Empirical evaluation demonstrates significant improvements in employer hiring accuracy. Crucially, it establishes a reproducible, auditable foundation for equitable assessment—enhancing both validity and procedural fairness for candidates while enabling rigorous, transparent evaluation of hiring systems.

This study addresses the ambiguity in leadership roles within human-AI collaborative decision-making, which often leads to unclear accountability and governance risks. It proposes a five-tier leadership spectrum—ranging from fully human to centaur, peer, minotaur, and fully AI-led configurations—that formally structures human-AI collaboration into an actionable leadership model. Introducing the concept of “co-adaptability,” the framework captures the capacity of humans and AI to jointly evolve within heterogeneous teams. Grounded in organizational theory and conceptual modeling, it incorporates team size, architecture, and capability heterogeneity to enable strategic leaders to identify their current decision-making configuration, monitor its dynamic drift, and assess alignment with task demands, thereby enhancing the governance efficacy of human-AI collaboration.

This study addresses the challenges of unstructured team formation, which often undermines project outcomes due to mismatches in member interests and insufficient skill coverage. To overcome these limitations, the authors propose a novel three-stage approach: first, collecting students’ self-reported interests and skill assessments; second, leveraging a large language model (LLM) to automatically extract the requisite skills from project descriptions; and third, applying a dynamic assignment algorithm that jointly optimizes both skill coverage and preference alignment. This work represents the first integration of LLM-based skill inference with a dynamic team-formation mechanism, explicitly ensuring required competencies while accommodating individual preferences. Preliminary evaluation demonstrates that teams formed using this method significantly outperform those generated by random assignment, manual grouping, and established baselines such as CATME in both skill coverage and preference satisfaction.

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 widespread adoption of artificial intelligence is blurring organizational role boundaries and eroding “invisible work”—such as mentoring and feedback—that underpins professional development and cultural health. Through semi-structured interviews with 24 product professionals in technology firms and subsequent thematic analysis, this study systematically uncovers AI’s dual impact: while enhancing peer-level collaboration, it simultaneously weakens traditional mechanisms of career support. To address these tensions, the research introduces a strategic framework that renders invisible work visible and offers actionable interventions for organizations, leaders, and individuals. These measures aim to preserve cultural sustainability without compromising operational efficiency in AI-integrated workplaces.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

This study addresses the inadequacy of the current U.S. Department of Defense software acquisition pathways in effectively managing the unique challenges posed by artificial intelligence systems—particularly their data dynamism, model evolution, and governance requirements. Through scenario-based policy analysis, the authors embed a hypothetical AI-enabled project into critical junctures of the existing acquisition process to systematically evaluate how policies translate into practice. The analysis reveals that core guidance documents lack operational specificity, while AI-related controls are fragmented across supplementary materials, leading programs to rely on inconsistent local interpretations. To bridge this gap, the paper proposes a dedicated AI acquisition sub-pathway alongside targeted documentation enhancements, substantially aligning policy with practice in areas such as data provenance, lifecycle management, and human oversight.