Damjan Fujs

Software engineering involves more than coding. It encompasses planning, development, communication, and process management. Scrum, the most widely adopted agile methodology, helps teams deliver value iteratively, yet practitioners often struggle with challenges such as maintaining requirement clarity, reducing cognitive load, and managing communication overhead. As artificial intelligence (AI) becomes increasingly integrated into the software engineering lifecycle, its potential to improve productivity, quality, and decision-making is gaining significant attention. Moreover, Scrum offers a structured yet flexible framework, but it remains unclear which AI features can most effectively support its practices in real-world settings. Therefore, this study addresses that gap by identifying and prioritizing key Scrum AI Support Features (SAISFs) based on industry needs. A two-phase research approach was used. First, a focus group with five software engineering industry experts identified 18 relevant SAISFs. Second, a survey using the Kano methodology was conducted with 344 experienced Scrum practitioners to evaluate and prioritize these features. The results were analyzed across three Scrum team size groups: small ( < = 6), medium (7–10), and large (11+), and four functional SAISF groups: Requirements Support (R), Development Support (D), Communication Support (C), and Scrum Process Support (S). The research also provides prioritization of SAISFs according to Scrum roles. Our findings offer actionable insights for designing AI-enhanced tools tailored to Scrum teams, highlighting the importance of considering team size and Scrum roles when prioritizing AI features. This study contributes to the agile software engineering literature by offering a practitioner-informed foundation for integrating AI into Scrum-based project environments. Future Scrum tools may become adaptive and context-aware, automatically tailoring workflows, predicting bottlenecks, and optimizing team communication and performance.

Purpose: This study aims to address the limitations of current Industry 4.0 evaluation approaches, which often rely on a one-size-fits-all philosophy. It introduces a novel situational evaluation method tailored to the unique contexts of organizations, with a focus on aligning Industry 4.0 initiatives with strategic goals. Design/methodology/approach: A case study methodology was employed involving a large multinational company with over 30 subsidiaries in 14 countries across Europe and Asia. The company operates in three core EU industries: automotive, electric power transformers, and construction. The study involved the entire top management team (TMT) in order to comprehensively assess the effectiveness of the proposed evaluation approach. Findings: The results demonstrate that the TMT highly valued the customized nature of the situational evaluation approach. The proposed Industry 4.0 evaluation approach significantly improved the alignment of the organization's strategic objectives between TMT members. Originality/value: The study provides a novel approach to Industry 4.0 evaluation that starts with selecting and structuring digital maturity dimensions according to the needs, context, and characteristics of a specific enterprise; continues with evaluations of individual TMT use and completes with group discussion to achieve final strategic alignment and address common challenges in Industry 4.0 adoption (TMT alignment, workforce readiness, and technology integration).

Artificial intelligence (AI) is transforming traditional medicine, particularly in radiology. Its integration across patient care stages has made it increasingly ubiquitous. The European Union’s (EU) AI Act will additionally regulate AI-enabled solutions within the EU. However, without standardized guidelines, the Act’s flexibility poses practical challenges for providers and deployers, leading to inconsistencies in meeting requirements for high-risk systems like radiology AI, potentially impacting patients’ fundamental rights and safety.

This study investigates how access to Large Language Models (LLMs) and varying levels of professional software development experience affect the prioritization of cybersecurity requirements for web applications. Twenty-three postgraduate students participated in a research study to prioritize security requirements (SRs) using the MoSCoW method and subsequently rated their proposed solutions against multiple evaluation criteria. We divided participants into two groups (one with and the other without access to LLM support during the task). Results showed no significant differences related to LLM use, suggesting that access to LLMs did not noticeably influence how participants evaluated cybersecurity solutions. However, statistically significant differences emerged between experience groups for certain criteria, such as estimated cost to develop a feature, perceived impact on user experience, and risk assessment related to non-implementation of the proposed feature. Participants with more professional experience tended to provide higher ratings for user experience impact and lower risk estimates.

An increasingly aging population, widespread internet connectivity, and an increasing number of cybersecurity incidents bring many challenges for the future of information security. Older adults, in particular, are a highly vulnerable demographic, often targeted by cyber attackers. This paper aims to develop an approach that enables the identification of different groups of users of information systems according to their requirements for information and cyber security training (ICST). To achieve this, we surveyed 784 older adults residing in the UK recruited through the Prolific platform. First, S-HAIS-Q (Shortened Human Aspects of Information Security Questionnaire) was used to assess their information security performance. Through hierarchical clustering and k-means clustering, we identified five distinct groups. Additionally, predictive clustering trees (PCTs) were employed to simultaneously perform clustering and classification, providing deeper insights into the characteristics of these groups. This approach provides a segmentation framework highlighting the potential for customizing ICST to address the diverse needs of different groups of older adults. The results of our study indicate that it is possible to tailor and optimize ICST even among older adults who are more proficient in computer use than the general older adult population.

This article is an extended abstract of the doctoral dissertation entitled “Tailoring security-related software and training requirements to users based on their categorization” (Fujs, 2024). Security has traditionally been ensured by technical solutions in the concluding stages of software development. The fact that security is considered an additional function means that a vulnerability is fixed with security patches as soon as it occurs. However, the importance of human factors is increasingly being recognized, as technical solutions alone are not enough to close security gaps. In order to address this shortcoming, we proposed an approach that simultaneously addresses technical as well as human aspects - already in the initial stages of software development.

This work reports on a cross-sectional study on device proficiency, support availability and cybersecurity competence of older adult users of smartphones and/or tablets. Results indicate that cybersecurity competence is associated with both device proficiency and support availability although the variance explained is relatively low. There were no differences in cybersecurity competence between users and non-users of either mobile devices. Users of both smartphones and tablets had significantly higher device proficiency than non-users. Users of tablets had significantly higher support availability than non-users while there were no significant differences between users and non-users of smartphones.

This work reports on a cross-sectional study on device proficiency, support availability and cybersecurity competence of older adult users of smartphones and/or tablets. Results indicate that cybersecurity competence is associated with both device proficiency and support availability although the variance explained is relatively low. There were no differences in cybersecurity competence between users and non-users of either mobile devices. Users of both smartphones and tablets had significantly higher device proficiency than non-users. Users of tablets had significantly higher support availability than non-users while there were no significant differences between users and non-users of smartphones.