Abstract

Abstract
Context: Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers’ perspective is crucial to aligning education with industry needs. Objective: To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method: A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results: The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master’s level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion: The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.

Abstract
Experience in teaching functional programming (FP) on a relational basis has led the author to focus on a graphical style of expression and reasoning in which a geometric construct shines: the (semi) commutative square. In the classroom this is termed the magic square (MS), since virtually everything that we do in logic, FP, database modeling, formal semantics and so on fits in some MS geometry. The sides of each magic square are binary relations and the square itself is a comparison of two paths, each involving two sides. MSs compose and have a number of useful properties. Among several examples given in the paper ranging over different application domains, free-theorem MSs are shown to be particularly elegant and productive. Helped by a little bit of Galois connections, a generic, induction-free theory for ${\mathsf{foldr}}$ and $\mathsf{foldl}$ is given, showing in particular that ${\mathsf{foldl} \, {{s}}{}\mathrel{=}\mathsf{foldr}{({flip} \unicode{x005F}{s})}{}}$ holds under conditions milder than usually advocated.

Abstract
Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Abstract
In recent years, companies have faced increasing pressure from globalization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the efficient integration of technology, achieved through digital processes and collaboration tools. Digital transformation has emerged as a critical element for maintaining competitiveness as economies become increasingly digital. To succeed in this ever-evolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced technologies like Artificial Intelligence (AI) and Web Technologies into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and streamlined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how advanced technologies can predict and adapt organizational training needs to improve competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training recommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, addressing this issue promotes a more inclusive and empowering work environment, enabling employees to thrive in an increasingly digital world. © 2025 Elsevier B.V., All rights reserved.

Abstract
In the pharmaceutical industry, noncompliance with any good manufacturing practice (GMP) leads to deviation, resulting in potential retention of finished product batches, reprocessing, or rejection-consequently increasing lead time and cost. This study aimed to outline a strategy to define, classify, and mitigate recurrent deviations occurring more than once within 12 months. This research followed an action research methodology, carried out within a Portuguese pharmaceutical company. A transversal analysis of the deviation management process was conducted across three phases: recording, investigation, and conclusion. The intervention included defining objective recurrence criteria, developing investigation models based on structured problem-solving, and redesigning the deviation management information system. The implementation decreased recurrent deviations by 78 percent, and a new process was established, facilitated by the participation and involvement of everyone in the organization. This article introduces pioneering contributions to the pharmaceutical industry by presenting novel criteria for assigning recurrence to recorded deviations and integrating Good Manufacturing Practices (GMP) with big data and analytics. Our approach enhances decision-making and manufacturing processes by structurally incorporating all types of causes beyond the human factor, emphasizing recurring deviations over extended periods. It defines conditions for correct deviation classification and constructs a decision matrix for investigation models. Additionally, it presents workshop management, providing analysis templates and a prototype information system, and outlines key steps to mitigate deviations, highlighting research limitations and future directions.