Fábio Paulo Basso

Artificial Intelligence (AI) has created new possibilities for improving Verification and Validation (V&V) activities in software engineering. Among recent advances, Large Language Models (LLMs) have gained attention for their potential to automate tasks such as test generation, defect detection, and requirements analysis. Although interest in these technologies has increased, their use in industrial environments is still limited and not well understood. Concerns remain about their reliability, transparency, and ethical implications, especially when applied in safety-critical or regulated domains. This paper reports an exploratory empirical study based on a survey with 39 software professionals from different sectors and company sizes in Brazil. The questionnaire investigates the reported use of conventional V&V tools, AI-based testing solutions, and LLM-supported practices, as well as perceptions of effectiveness, workflow integration, cost, and governance challenges. Respondents reported perceived benefits, such as support for automation, test-case creation, code understanding, and efficiency-oriented tasks, along with difficulties related to integration, transparency, reliability, hallucinations, over-reliance, data misuse, and governance. By documenting current practices, perceptions, and expectations, this study offers an exploratory empirical view of how AI and LLMs are being selectively incorporated into industrial V&V activities in Brazil. The findings may inform discussions on responsible AI adoption in software engineering and provide preliminary, practitioner-informed insights for future research and governance-oriented decision-making. Given the exploratory design, convenience sample, and self-reported data, these findings should be interpreted as indicative perceptions rather than statistically generalizable evidence.

O estudo aborda a avaliação de uma ferramenta web para customização e gerenciamento de rubricas, visando melhorar a compreensão sobre avaliação educacional. O objetivo é investigar a usabilidade da ferramenta com base em protótipos por meio de um grupo focal de docentes experientes, buscando insights para aprimorar o desenvolvimento. Conduzimos uma técnica de grupo focal com cinco docentes, seguindo um processo exploratório de avaliação. Como resultado, o feedback qualitativo identificou pontos fortes e limitações da ferramenta, fornecendo direcionamentos para melhorias. O estudo evidenciou a importância da avaliação participativa para aprimorar ferramentas educacionais, e a necessidade de clareza e objetividade nas avaliações.

Context: Agribusiness is a key pillar of the Brazilian economy, significantly contributing to national growth and wealth generation. This sector heavily relies on agricultural machinery technologies to enhance productivity and operational efficiency. Problem: The high costs of acquiring and maintaining agricultural machinery restrict access for small-scale farmers. Furthermore, idle equipment generates significant passive costs, creating an additional barrier to its efficient use. Solution: This study proposes a tool for advertising agricultural machinery rentals, grounded in the principles of the sharing economy. The platform connects equipment owners with idle machinery to renters in need of such resources. IS Theory: The study is based on the DeLone and McLean Information Systems Success Model, which highlights system quality, information quality, and user satisfaction as critical factors for success. Method: A qualitative heuristic evaluation approach was used, focusing on experts’ perceptions of the system prototype to identify usability barriers and opportunities for improvement. Summary of Results: Most usability issues were classified as low or moderate severity, suggesting they can be resolved without significantly impacting the user experience. Violations were most frequent in consistency, system visibility, and error prevention, highlighting areas for prioritized improvement. Contributions and Impact on IS Field: This study advances the Information Systems field by demonstrating how sharing economy principles can inform digital solutions for agribusiness. It validates the application of the DeLone and McLean model in rural contexts and offers insights for addressing usability challenges, fostering innovation and accessibility in agricultural systems.

Context: The Brazilian National Council of Education’s Resolution Nº. 7/2018 mandates the integration of outreach activities into higher education curricula, effective from 2023. This requirement has created a need for efficient solutions to manage and support outreach activities within universities. Problem: Managing outreach activities manually is challenging, often leading to inefficiencies and missed opportunities for student engagement. The absence of a comprehensive system for managing these activities makes it difficult for coordinators to track and validate students’ participation effectively. Solution: This study presents the development and evaluation of a web-based IS designed to support the management of outreach programs and projects within academic institutions, particularly focusing on use during large-scale events like an Academic Week. Theory of IS: The research is based on Soft Systems Theory (SST), which helps address the complex social and organizational issues involved in managing university outreach activities. Method: A case study method was employed during our university’s AcademicWeek, involving over 300 users. Data were collected through direct observation and a structured survey, utilizing the AttrakDiff technique to evaluate user satisfaction with usability and functionality. Summary of Results: The Web-based IS demonstrated effective support for managing outreach activities and received positive feedback from users. Key insights were collected, including suggestions to improve navigability, performance optimization, and user support materials. Contributions and Impact in the IS Area: This research contributes a tailored management Web-based IS that addresses specific needs in academic outreach, potentially enhancing the efficiency and accessibility of outreach activities. The findings provide valuable insights for further development and set the groundwork for similar applications in educational institutions.

Automation is essential for productivity and safety in the oil industry, but designing control systems often requires multiple software tools, leading to redundant plant modeling (costing time and money) and creating possible inconsistencies. The Model-Driven Engineering for Petrochemical Industry Automation (M4PIA) approach streamlines automation design by enabling the interoperability of models between different tools. Currently, M4PIA integrates EMSO for process simulations and MPA for deploying real plant applications. Besides, it supports high-level, graphical automation models design, also providing a component library. This paper aims to detail M4PIA, positioning it within the state-of-the-art, and illustrate its application through the deployment of an oil and gas automation system. This case study highlights M4PIA’s ability to handle complex, real-world systems, demonstrating the platform’s capability to optimize modeling stages through domain-specific languages (DSLs) and automated transformations. The results show that M4PIA not only reduces development time but also enhances system reliability and maintainability. By bridging simulation and deployment tools, M4PIA establishes a solid foundation for efficient and robust application development in the oil and gas sector. This platform represents a significant step forward in advancing model-driven engineering for industrial automation.

Este estudo desenvolve e avalia API Gateways para o controle de acessos em uma arquitetura de microsserviços. O trabalho compara a API Gateways desenvolvida e o Kong API Gateway, utilizando a ferramenta Locust para testes de desempenho com workloads de 100 e 500 usuários simultâneos. Foram analisados os endpoints Mobile e Webservice, com métricas de número de requisições, tempo de resposta e uso de recursos computacionais. Os resultados mostram que ambas as soluções são eficientes, com o Kong se destacando no uso de rede, enquanto a API Gateways desenvolvida demonstrou melhor consumo de memória. Conclui-se que uma API Gateways é fundamental para o controle de acessos e a segurança na arquitetura de microsserviços.

Durante a pandemia de COVID-19 em 2020, diversas organizações desenvolveram sistemas para auxiliar na detecção do vírus. A plataforma PredictCovid foi criada com o objetivo de apoiar profissionais de saúde na triagem e identificação de casos de COVID-19 por meio da análise de raios-x, utilizando tecnologias de Inteligência Artificial (IA). Com o intuito de aprimorar essa solução, desenvolvemos o aplicativo X-Ray Covid19, utilizando Flutter, Firebase e TensorFlow Lite, proporcionando uma experiência mais prática e acessível. A avaliação de usabilidade, baseada nas 10 heurísticas de Nielsen, indicou uma boa experiência do usuário, com destaque para a visibilidade do sistema e o design simples. Além disso, o aplicativo tem se mostrado um avanço importante no suporte aos profissionais de saúde durante a pandemia.

Este estudo apresenta os resultados da disciplina de Resolução de Problemas I, cujo ementário foca na Engenharia de Requisitos do curso de Engenharia de Software da Universidade Federal do Pampa, com ênfase no projeto extensionista e na colaboração com agentes externos. Um aspecto fundamental do projeto foi a participação ativa de uma enfermeira como stakeholder, cuja experiência e conhecimentos foram importantes para a definição dos requisitos de um software voltado ao tratamento de lesões por pressão. A colaboração com a enfermeira permitiu uma coleta de requisitos mais precisa e alinhada às necessidades práticas do contexto de saúde, facilitando a especificação de requisitos do projeto que visa melhorar o cuidado com pacientes e a gestão de lesões. O projeto demonstra a eficácia das atividades extensionistas na conexão entre a academia e a prática profissional, evidenciando o potencial para o desenvolvimento de soluções aplicadas a problemas reais no setor de saúde.

Due to Resolution Nº 7 of 2018 by the National Council of Education (CNE) of Brazil, outreach curricularization is mandatory since 2023. This study aims to evaluate the usability of our tool, which is designed to support the management of academic outreach activities, projects and programs. A Groupbased Expert Walkthrough was conducted with potential users from our university’s academic community. The goal is present our proposal web-based tool as well as how we evaluating the usability in our tool, while allowing subjects to offer suggestions for improvements and new ideas. Valuable qualitative feedback was obtained, guiding the development of the tool, prioritizing and validating requirements. The usability evaluation provided insights to enhance the tool’s learnability and user-friendliness for the academic community.