Eva-Maria Schön

Juliana Fraislebem, Mali Senapathi, Michael Neumann et al.

In response to the growing reliance on Agile and DevOps methodologies for enhancing software delivery speed and quality, this study investigates the persistent challenges and viable solutions associated with their integration. Although Agile promotes iterative development and customer responsiveness, and DevOps emphasizes automation and operational efficiency, their convergence in practice often presents significant organizational, structural, and technical hurdles. This research employs a qualitative methodology grounded in semi-structured interviews with six seasoned industry professionals across Brazil and Germany, each with extensive experience in both Agile and DevOps domains. The study identifies four core categories of integration challenges: Cultural & Organizational Barriers, Structural Constraints, Process \& Method Complexity, and Technical Limitations. Additionally, it offers four major solution domains: Team Structure & Autonomy, Culture & Collaboration, Process & Change Management, and Automation & Infrastructure. The findings underscore the importance of cultural alignment, proactive monitoring, automation, and other practices in mitigating integration friction. The results contribute to a deeper understanding of the Agile-DevOps interface and offer practical insights for software organizations seeking to navigate this complex transition effectively.

Karen Eilers, Michael Neumann, Eva-Maria Schön et al.

Agile software development has been shaped by the interplay between academic research and industrial practice for over two decades, yet notable gaps persist between both domains. This paper focuses on three research-practice gaps: the theory gap, the time gap, and the transfer gap. To address these, the 2nd Agile Practice & Research Workshop was held at the International Conference on Agile Software Development (XP) 2026 in São Paulo, Brazil, bringing researchers and practitioners together to identify root causes and develop joint solutions. Building on two preceding sessions in which contributions of participants had been presented, participants engaged in a structured collaborative session, working in small groups on one of the three gaps and reflecting on possible causes and remedies. The organizers synthesized the results into four propositions for improving the research-practice intersection: (1) improving scientific communication, (2) aligning research more closely with emerging industrial needs, (3) creating stronger incentives for sustained collaboration, and (4) integrating educational approaches into research practice. From these, three calls for research were formulated: (a) broader adoption of open science practices for transparency, reproducibility, and cumulative evidence; (b) higher empirical quality standards through stronger theoretical grounding and rigorous design; and (c) more explicit, value-oriented contributions that clearly articulate their practical and scientific relevance. The paper offers both a summary of the workshop and a call to strengthen research-practice collaboration.

Michael Neumann, Lasse Bischof, Maria Rauschenberger et al.

The rapid development of GenAI technologies is transforming learning, assessment, and academic production in higher education. Despite increasing student adoption, many institutions lack operational mechanisms to systematically align regulations and curricula with evolving generative artificial intelligence practices, creating regulatory ambiguity and academic integrity risks. This study investigates how students utilize generative artificial intelligence tools in computer science-oriented disciplines and develops a structured, lightweight framework supporting institutional adaptation to pervasive GenAI usage. We conducted a case study at the University of Applied Sciences and Arts Hannover (Germany), combining document analysis with an online survey (N = 151) targeting Business Information Systems and E-Government students. Quantitative responses were analyzed statistically, while open-ended responses underwent thematic synthesis. Generative artificial intelligence adoption was widespread, with ChatGPT as the dominant tool. Students primarily used generative artificial intelligence for research assistance, programming support, and text processing. However, substantial policy uncertainty was observed: many students were unaware of or unsure about institutional generative artificial intelligence regulations. Document analysis revealed regulatory gaps, ambiguous terminology, and inconsistencies between formal rules and teaching practices. To address these shortcomings, we propose the AI Adoption Framework for Higher Education, an iterative and operational model integrating document analysis, empirical observation, synthesis of findings, and targeted updates of regulations and curricula. The framework addresses governance, assessment validity, and academic integrity under generative artificial intelligence conditions and provides practical guidance for institutional adaptation.