Arboviruses such as dengue, Zika, and chikungunya remain a serious public health challenge in Brazil and other Latin American countries. Effective surveillance and vector control require integrated information systems to support planning and monitoring efforts. This study presents a heuristic evaluation of a web-based system developed for arbovirus control coordinators in Brazil, aiming to identify interface limitations and propose improvements based on user experience. The Design Science Research Methodology (DSRM) guided the study, and a structured questionnaire grounded in Nielsen’s Heuristics was applied. A total of 72 undergraduate students from technology-related programs participated in the evaluation. Each participant performed guided tasks within the system and provided feedback through both closed and open-ended questions. Results indicated positive perceptions regarding interface consistency and organization, while usability issues were identified in areas such as error prevention, flexibility, and feedback mechanisms. Statistical analysis revealed differences among participant groups, highlighting the influence of user profiles on perceived usability. The findings underscore the value of heuristic evaluation in improving public health digital tools and demonstrate the potential for adapting such systems to similar regional contexts across Latin America.

This study investigates the use of gamification as an active methodology in Software Engineering education, aiming to enhance engagement, motivation, and learning effectiveness. The research proposes guidelines to assist educators in planning gamified activities, covering aspects such as scenario definition, player profiles, and game elements. The guidelines were validated through an exploratory study with teachers, who evaluated their effectiveness, ease of use, and adaptability to the educational context. Results indicate that the guidelines facilitate the integration of gamification in teaching. While validated with a reduced sample size and focused on Software Engineering, the findings highlight their potential for broader interdisciplinary application, offering a practical resource to foster more dynamic and engaging learning experiences.

The adoption of microservice architecture has seen a considerable upswing in recent years, mainly driven by the need to modernize legacy systems and address their limitations. Legacy systems, typically designed as monolithic applications, often struggle with maintenance, scalability, and deployment in-efficiencies. This study investigates the motivations, activities, and challenges associated with migrating from monolithic legacy systems to microservices, aiming to shed light on common practices and challenges from a practitioner's point of view. We conducted a comprehensive study with 53 software practitioners who use mi-croservices, expanding upon previous research by incorporating diverse international perspectives. Our mixed-methods approach includes quantitative and qualitative analyses, focusing on four main aspects: (i) the driving forces behind migration, (ii) the ac-tivities to conduct the migration, (iii) strategies for managing data consistency, and (iv) the prevalent challenges. Thus, our results reveal diverse practices and challenges practitioners face when migrating to microservices. Companies are interested in technical benefits, enhancing maintenance, scalability, and deployment processes. Testing in microservice environments remains complex, and extensive monitoring is crucial to managing the dynamic nature of microservices. Database management remains challenging. While most participants prefer decentralized databases for autonomy and scalability, challenges persist in ensuring data consistency. Additionally, many companies leverage modern cloud technologies to mitigate network overhead, showcasing the importance of cloud infrastructure in facilitating efficient microservice communication.

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. MOA4PLA treats the PLA design as a multi-objective optimization problem. At the end of the search process several PLA design alternatives are presented to the decision maker, difficulting the decision about which PLA alternative best fits with his/her needs. In this sense, this work proposes the usage of clustering algorithms to group PLAs design alternatives, according to their characteristics and assist the decision maker in the choice of one PLA design for the SPL. For this purpose, an empirical study was carried out, involving quantitative and qualitative experiments. Such an study integrated the K-Means++ and DBSCAN clustering algorithms in the MOA4PLA approach. The results of the experiments were promising, since an appropriate grouping of the solutions can be quantitatively observed, and also, qualitatively, the suitability of the solutions to the decision makers needs was verified.

The architectural properties of product‐line architecture (PLA) design have been successfully optimised by multi‐objective genetic algorithms (GAs). Memetic algorithms (MAs) extend GA by adding a local search after the global search process. MA outperformed GA in the context of class modelling, software testing and the next release problem. However, no studies on the application of MAs for PLA design optimisation were found in the literature. In light of this, the authors performed an exploratory study, where MA was used to apply design patterns for a search‐based PLA design (SBPD) and achieved promising results. From the obtained results, they adjusted the MA‐based implementation. This study aims at investigating if the MA is more profitable to SBPD than GAs. Two empirical studies that involve four PLA designs were carried out for this task by varying the pair of objective functions. Empirical results show that MA achieved satisfactory solutions, despite being influenced by the original PLA design.

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, OPLA-Tool was developed to automatically identify the best alternatives for a PLA design using multi-objective evolutionary algorithms, based on genetic algorithms (GA). From an original PLA, OPLA-Tool obtains alternative designs to improve the original one in terms of the objectives selected for optimization. In a recent study, we extend OPLA-Tool to add a memetic algorithm (MA) and promising empirical results were obtained. However, the results allow us to hypothesize that including feature modularization as an objective to be optimized by MA could obtain even better solutions. In addition, it is interesting to know the experts' opinion about PLA designs automatically obtained, what has not been done yet. Thus, the objective of this work is twofold: to investigate the aforementioned hypothesis and to conduct the first qualitative evaluation of the PLA design solutions automatically obtained by search algorithms. An empirical study involving MA and GA was carried out with four different PLA designs. MA presented the best solutions according the quality indicators used in the quantitative analysis. The results of the qualitative evaluation showed that the optimized solutions are well evaluated by the experts. After both analyzes, the hypothesis could be confirmed.

Current paper describes the use of a feature selection technique to reduce the number of features while the goodness set is selected on a framework for forensic handwriting identification. A sequential forward search and an evaluation criterion based on dependency were used to obtain a goodness subset (GS) to improve the identification rate. The accuracy of the system applied to 100 different writers and taking account all features (N = 81) is 58%, whereas the accuracy based on goodness subset (GS) is 80% applied to the same number of writers. The validation of results was verified initially against all the features and later against some empirically set of features. Results are comparable to others in the literature on graphometric features.