Software engineering education (SEE) requires a careful balance between theoretical concepts and practical application, and this is especially true of teaching software requirements. However, traditional teaching methods, such as lectures, often fail to engage students and foster the practical skills necessary for real world software development. We present a pilot study on the application of active learning methodologies in a software engineering course involving 21 midlevel undergraduate informatics students. The approach covers the entire requirements engineering process, from stakeholder identification to effort estimation techniques, to make learning more experiential and applied. We split each class into three segments: (i) theoretical instruction; (ii) collaborative group activities, including role playing to simulate real world dynamics and encourage students to apply concepts in context; and (iii) feedback and guided reflection. The course capstone was a requirements elicitation activity conducted in an actual real world scenario, aimed at consolidating and demonstrating the skills developed throughout the course. Initial results, as shown by ungraded assessment of the elicitation activity and by a student survey, indicate that this structured active learning approach allowed us to achieve the intended learning outcomes and was also well received by the students. Our piloted approach shows promise to provide a replicable SEE method that enhances student engagement, promotes deeper learning, and strengthens the development of practical skills in requirements engineering.

In the context of software engineering, analyzing causality in the dynamics of development teams is essential for optimizing performance. Causality involves understanding how certain factors (organizational structures, individual interactions) directly influence the team's performance, allowing for the identification and implementation of effective improvements in development processes. Identifying and understanding the underlying causes of heterogeneous effects in team dynamics is essential for improving collaboration and productivity. The lack of specific methodologies to explore these causal relationships in complex organizational settings limits the ability of project leaders to implement effective changes. This article describes a method that uses causal inference in organizational mining to assess software development teams. Data are analyzed to identify interactions and causal factors affecting role dynamics, and causal inference techniques are employed to evaluate the effects of improvement actions. The effectiveness of this approach was confirmed with a pilot software development team at a Chilean payment processing company. By employing causal organizational analysis methods, managers were able to select more focused strategies, based on a deep and detailed understanding of the underlying causal dynamics. This work contributes to the field of software engineering by introducing a structured and causal approach to analyze team dynamics and providing project managers with tools to address the underlying factors that hinder team effectiveness.

Process deviations can be difficult and costly to identify. Therefore, it's imperative for organizations to detect temporary deviations during execution and understand their causal relationships. This enables decision-makers to implement targeted corrective actions. This article presents a method to construct a causal graph for the analysis of process variants, combining techniques from process mining, unsupervised machine learning, and causal discovery and inference. This graph is not susceptible to Simpson's paradox, where aggregating the feature space can lead to incorrect interpretations of causal effects. The technique has been initially validated with a well-known event log, namely a loan application process taken from the BPI Challenge 2017 and containing 16,299 records. This pilot run successfully identified the causal variables and their direction. Wider use of this approach will allow organizations to interpret and estimate the causal effect of an action plan on process variants with temporary deviations.