Abstract Software engineering education widely adopts Agile and project-based learning approaches. However, questions remain about how student roles, perceptions, and the use of Large Language Models (LLMs) relate to learning outcomes. In this exploratory study, we investigate how students engage in a semester-long project-based agile course by examining three core elements: their software development roles and Scrum roles, their expectations and actual use of LLMs, and their perceptions of teamwork and learning practices. We focus on differences between below-average and above-average performing students, thereby linking project outcomes to effort, perceptions, and role-specific engagement. Our analyses show that student effort is strongly associated with performance, and that the distribution of typical software development roles relates to meaningful differences in both engagement and outcomes. The findings show that front-end developers tend to exhibit lower engagement and performance, indicating a need for role-specific support. Although students were expected to use LLMs, actual usage was limited, especially in complex tasks like testing and deployment, revealing a clear expectation–use gap rather than allowing conclusions about its underlying causes. Higher-performing students reported more positive perceptions of team collaboration and Scrum practices, suggesting a link between engagement, perception, and outcomes. Together, these findings contribute to understanding how roles, perceptions, and LLM expectations interact with student performance in agile, project-based settings.

Abstract 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.

Abstract Effort estimation is an important activity in agile software development. The goal of the presented study was to determine the influence of individual competence on software development effort estimation. In particular, we measured both the accuracy of effort estimation and the duration of the estimation process itself, both for three different estimation methods. The subjects of our study were teams of students of a graduate‐level software engineering course at the University of Ljubljana, Faculty of Computer and Information Science. Based on the grades that individual students attained in their undergraduate study, we classified each team as “high‐competence” or “low‐competence” and additionally as “heterogeneous” or “homogeneous” (the criterion here being the variance of the members' average grades). We found out that there was no significant difference in effort estimation accuracy neither between high‐competence and low‐competence teams nor between heterogeneous and homogeneous teams, regardless of which estimation method was used. However, high‐competence teams spent significantly less time on effort estimation than low‐competence ones. Likewise, for two of the employed estimation methods, heterogeneous teams completed effort estimation in a significantly shorter time than homogeneous teams. These results might benefit both academic and professional community.

Information security is one of the key areas of consideration to assure reliable and dependable information systems (IS). Achieving an appropriate level of IS security requires concurrent consideration of the technical aspects of IS and the human aspects related to the end users of IS. These aspects can be described in the form of information security requirements. We propose an approach that helps select and balance information security software requirements (iSSR) and information security training requirements (iSTR) according to the information security performance of end users. The approach was tested in an experiment involving 128 IS professionals. The results showed that using the proposed approach helps IS professionals with limited experience in information security make significantly better decisions regarding iSSR and iSTR.

Published studies on agile effort estimation predominantly focus on comparisons of the accuracy of different estimation methods, while efficiency comparisons, i.e. how much time the estimation methods consume was not in the forefront. However, for practical use in software development, the time required can be a very important cost factor for enterprises, especially when the accuracy of different agile effort estimations is similar. In this study, we thus try to advance the current standard accuracy comparison between methods by introducing efficiency, i.e. time it takes to use a method as an additional dimension of comparison. We conduct this comparison between three agile effort estimation methods that were not yet compared in the literature, namely, Planning Poker, Bucket System and Affinity Estimation. For the comparison, we used eight student teams with 29 students who had to use all the effort estimation methods during the course where they had to finish a programming project in 3 weeks. The results indicate that after the students get used to using the different methods the accuracy between them is not statistically significantly different, however, the efficiency is. On average, Bucket System and Affinity Estimation methods take half as much time as Planning Poker.

Abstract Companies often claim to follow specific software development methodologies (SDM) when performing their software development process. These methodologies are often supported by dedicated tools that keep track of work activities carried out by developers. The purpose of this paper is to provide a novel approach that integrates analytical insights from both the perceptions of SDM stakeholders and software development tools logs to provide SDM improvement recommendations. This paper develops a new process improvement approach that combines two significantly different sources of data on the same phenomenon. First, it uses a questionnaire to gather software development stakeholder SDM perceptions (managers and developers). Second, it leverages process mining to analyze software development tools logs to obtain additional information on software development activities. Finally, it develops recommendations based on concurrent analysis of both sources. Our novel process improvement approach is evaluated in three directions: Does the presented approach (RQ1) enable managers to gain additional insights into employees' performance, (RQ2) deliver additional insights into project performance, and (RQ3) enable development of additional SDM improvement recommendations? We find that integrated analysis of software development perception data and software development tools logs opens new possibilities to more precisely identify and improve specific SDM elements. The evaluation of our novel process improvement approach follows a single case study design. Our approach can only be used in enterprises in which software development tools logs are available. The study should be repeated in different cultural settings. We practically show how concurrently analyzing data about developer SDM perceptions and event log data from software development tools enables management to gain additional insights in the software development process regarding the performance of individual developers. The main theoretical contribution of our paper is a novel process improvement approach that effectively integrates data from management and developer perspectives and software development tools logs.