Programa de Pós-Graduação em Sistemas e Computação
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Doctoral Thesis Extension and reduction of fuzzy operators via retractions(Universidade Federal do Rio Grande do Norte, 2025-10-16) Silva, Ana Shirley Monteiro da; Santiago, Regivan Hugo Nunes; Sola, Nicanor Humberto Bustince; http://lattes.cnpq.br/7536988783793885; http://lattes.cnpq.br/1836142304259907; Bedregal, Benjamin René Callejas; http://lattes.cnpq.br/4601263005352005; Bergamaschi, Flaulles Boone; http://lattes.cnpq.br/2610624203466231; Dimuro, Graçaliz Pereira; https://orcid.org/0000-0001-6986-9888; http://lattes.cnpq.br/9414212573217453; Santos, Hélida Salles; https://orcid.org/0000-0003-2994-2862; http://lattes.cnpq.br/2096843997457737; Costa, Valdigleis da Silva; https://orcid.org/0000-0001-8738-5836; http://lattes.cnpq.br/2633660012976715This thesis addresses the problem of extension and reduction of fuzzy operators defined on partially ordered sets, a relevant topic in fuzzy logic due to its connection with the preservation of structures and properties. The work is inspired by the method of Palmeira and Bedregal, who used retractions and sections to propose an extension method (up to isomorphism) of fuzzy operators on bounded lattices. We generalize this method to partially ordered sets and introduce the notion of function reduction, conceived as the dual perspective of extension. We also characterize conditions for preserving classes and properties of fuzzy operators and establish a partial duality between extension and reduction. Furthermore, we introduce the notions of quasi-vector spaces, partially ordered quasi-vector spaces, and conditional monotonicity for functions defined on these spaces, which opens new perspectives for applications in fuzzy logic and related areas.Doctoral Thesis Uncovering the relationship between continuous integration and machine learning projects(Universidade Federal do Rio Grande do Norte, 2025-09-04) Bernardo, João Helis Júnior de Azevedo; Kulesza, Uirá; Costa, Daniel Alencar da; http://lattes.cnpq.br/4134189443162798; http://lattes.cnpq.br/0189095897739979; http://lattes.cnpq.br/7977641264944574; Côgo, Filipe Roseiro; https://orcid.org/0000-0002-5494-685X; http://lattes.cnpq.br/9500095790815109; Pinto, Gustavo Henrique Lima; https://orcid.org/0000-0001-7598-2799; http://lattes.cnpq.br/1631238943341152; Barroca Filho, Itamir de Morais; https://orcid.org/0000-0003-1694-8237; http://lattes.cnpq.br/1093675040121205; Cacho, Nelio Alessandro Azevedo; http://lattes.cnpq.br/4635320220484649Continuous Integration (CI) is a cornerstone of modern software development. However, while widely adopted in traditional software projects, applying CI practices to Machine Learning (ML) projects presents distinctive challenges. Therefore, this thesis investigates the differences, challenges, and strategies of CI adoption in ML through four complementary studies, combining large-scale repository analysis with practitioner surveys. Study 1, analyzing 93 ML and 92 non-ML GITHUB projects, shows that ML projects have longer build durations and lower test coverage. Study 2, surveying 155 practitioners from 47 ML projects, identifies eight main differences in CI adoption, with challenges such as test complexity, infrastructure demands, and data handling. Study 3, based on responses from 450 practitioners across a diverse set of open-source projects, establishes a baseline for how CI affects pull request (PR) delivery time, finding that CI streamlines review and quality control but does not necessarily accelerate PR delivery. Study 4, analyzing 27 ML and 31 non-ML projects, reveals that ML projects have significantly longer delivery times and PR lifetimes, receive fewer PRs per release, and follow slower release cadences. Overall, while core CI principles remain relevant, ML projects require tailored practices, such as tracking model performance metrics, prioritizing test execution, and improving dependency management. The findings highlight the need for standardized guidelines to address these challenges and strengthen CI workflows in ML. By integrating quantitative data and practitioner insights, this thesis advances the understanding of CI in ML, paving the way for more effective and robust CI strategies in the ML domain.Doctoral Thesis Avaliação da experiência ótima de jogadores por meio de escalas de fluxo: revisão, classificação, diretrizes psicométricas, jogo digital e instrumentos psicométricos(Universidade Federal do Rio Grande do Norte, 2025-08-25) Marques, Fábio Phillip Rocha; Miranda, Leonardo Cunha de; https://orcid.org/0000-0003-1929-9391; http://lattes.cnpq.br/9064196799520278; https://orcid.org/0000-0002-3858-2056; http://lattes.cnpq.br/6005182984958373; Cavalcante, Everton Ranielly de Sousa; https://orcid.org/0000-0002-2475-5075; http://lattes.cnpq.br/5065548216266121; Brasil, Fabricio Lima; https://orcid.org/0000-0002-9984-1007; http://lattes.cnpq.br/5066712308449764; Mendes, Gabriel Alves Vasiljevic; Baranauskas, Maria Cecilia Calani; Pereira, RobertoFlow is an optimal psychological experience that is fundamental to engaging in activities such as digital games. Although games environments are favorable to flow, their assessment lacks robust, validated instruments specifically for this context. This thesis aimed to develop and validate instruments to assist researchers in assessing the flow experience in players. The methodology was based on two state-of-the-art reviews of physiological and psychometric approaches to flow assessment. Subsequently, guidelines for developing new assessment instruments were proposed and validated. A digital game was developed and validated as a platform for experimentation. Based on the identified gap, new psychometric instruments for self-assessment of flow in games were proposed and validated through an experimental study with players, which demonstrated validity and reliability. This work’s main contributions include: (i) a state-of-the-art review of psychometric and physiological instruments for flow evaluation; (ii) a comparative study that reviews the validity, reliability, and applicability of the psychometric instruments found for the context of flow evaluation in games; (iii) consolidated guidelines for the evaluation of flow assessment instruments; (iv) a software artifact, in the form of a game, for research in the area; and (v) new valid and reliable psychometric instruments, specifically designed for the evaluation of optimal experience of players.Doctoral Thesis A framework for semi-supervised data stream classification in non-stationary environments(Universidade Federal do Rio Grande do Norte, 2025-07-18) Gorgônio, Arthur Costa; Canuto, Anne Magaly de Paula; Vale, Karliane Medeiros Ovidio; https://orcid.org/0000-0001-9845-8156; http://lattes.cnpq.br/7907570677010860; http://lattes.cnpq.br/1357887401899097; http://lattes.cnpq.br/8213279977425231; Carvalho, Bruno Motta de; Araújo, Daniel Sabino Amorim de; Zanchettin, Cleber; Silva, Huliane Medeiros daMaster Thesis A migration metaprocess from a monolithic architecture to microservices(Universidade Federal do Rio Grande do Norte, 2025-06-26) Medeiros, Henrique David de; Batista, Thais Vasconcelos; Cavalcante, Everton Ranielly de Sousa; https://orcid.org/0000-0002-2475-5075; http://lattes.cnpq.br/5065548216266121; https://orcid.org/0000-0003-3558-1450; http://lattes.cnpq.br/5521922960404236; http://lattes.cnpq.br/5136480770028616; Ferraz, Carlos André GuimarãesMicroservice-based architectures have gained popularity for their ability to handle the complexity of cloud service-oriented systems and meet requirements for availability, maintainability, and scalability. However, the process of modernizing legacy systems to adopt microservices is often time-consuming and lacks a systematic approach. There is a need for clear guidance on which activities and artifacts are essential when migrating applications to microservices and how to establish a well-defined process to achieve the expected benefits. This work introduces the Metaprocess for Microservice Migration Kernel (M3K), which serves as a foundation for migrating monolithic applications to microservice architectures. The primary objective of this proposal is to establish a comprehensive metaprocess that development teams and organizations can use as a basis for defining their microservice migration processes. M3K leverages OMG’s Essence Standard, providing a solid framework for defining software development practices, activities, and work products, and facilitating the smooth transition to microservice architectures.Master Thesis Desenvolvimento de modelos de previsão de consumo para Smart Meter(Universidade Federal do Rio Grande do Norte, 2025-08-29) Sousa, Ewerton Leandro de; Kreutz, Márcio Eduardo; Cunha, Eduardo Nogueira; https://orcid.org/0000-0001-6991-7296; http://lattes.cnpq.br/7628513373242513; http://lattes.cnpq.br/6374279398246756; https://orcid.org/0009-0009-8188-6707; http://lattes.cnpq.br/1011490479268699; Pereira, Mônica Magalhães; http://lattes.cnpq.br/5777010848661813; Muller, Ivan; https://orcid.org/0000-0003-2914-0050; http://lattes.cnpq.br/1522479715721496Increasing energy consumption around the world presents a number of significant challenges. Smart Grids play a crucial role in creating more sustainable and resilient energy systems. These systems are essential to face various energy and environmental challenges, allowing for more efficient management of resources, reduction of waste and integration of renewable energy sources. This research proposes the development of prediction models for Smart Meter, a smart meter capable of performing precise measurements, collecting consumption data in real time and executing prediction algorithms locally. The device integrates technologies for wireless communication and machine learning techniques optimized for edge devices. One of the main contributions of this work is the demonstration that it is possible to perform complex energy consumption predictions directly on the measuring device, reducing the need for continuous transmission of raw data and relieving the load on centralized systems. This not only improves the operational efficiency of smart grids, but also offers benefits in terms of consumers’ privacy and data securityMaster Thesis Geração automática de questões de programação baseada em templates multicamadas(Universidade Federal do Rio Grande do Norte, 2025-04-30) Silva, Abner de Santana; Aranha, Eduardo Henrique da Silva; http://lattes.cnpq.br/9520477461031645; Lucena, Márcia Jacyntha Nunes Rodrigues; Silva, Thiago Reis daDeveloping programming skills in introductory courses requires frequent practice exercises, yet manually crafting questions is time-consuming and laborintensive. To address this challenge, this study investigates automatic question generation through multilayer templates supported by generative artificial intelligence, which suggests variations and provides feedback to students. A web-based tool was created that employs JSON files to define hierarchical templates and their question instances. A mixed-methods research design was adopted, including a case study with eight instructors to evaluate templateconstruction time, the number of questions generated, and perceptions gathered via questionnaires. Most instructors produced a complete template within 30 minutes, although they preferred adapting pre-existing models. The primary difficulties reported were the steep learning curve, direct JSON manipulation, and the absence of a graphical interface for visualizing layers. Future work involves expanding the template repository, developing a visual editor, and evaluating the solution with larger samples.Doctoral Thesis PandemAI: a machine learning-based framework for pandemic viral disease symptom dynamics analysis(Universidade Federal do Rio Grande do Norte, 2025-05-30) Marques, Julliana Caroline Gonçalves de Araújo Silva; Carvalho, Bruno Motta de; Abreu, Marjory Cristiany da Costa; https://orcid.org/0000-0001-7461-7570; http://lattes.cnpq.br/2234040548103596; http://lattes.cnpq.br/0330924133337698; http://lattes.cnpq.br/5554033822360657; Canuto, Anne Magaly de Paula; http://lattes.cnpq.br/1357887401899097; Oliveira, Luiz Affonso Henderson Guedes de; https://orcid.org/0000-0003-2690-1563; http://lattes.cnpq.br/7987212907837941; Barros, Maicon Herverton Lino Ferreira da Silva; https://orcid.org/0000-0002-0275-3298; http://lattes.cnpq.br/7992942343005315; Gendriz, Ignacio SanchesHistorically, pandemics have manifested in various forms, each impacting human societies around the world in different ways over time. Some pandemics are particularly remembered for their high mortality rates, widespread geographic reach, or prolonged duration. However, when dealing with pandemics caused by viruses, it is crucial to consider their inherent ability to mutate rapidly, generating different lineages and variants. In such a scenario, where the same disease can manifest in multiple forms, correctly identifying it becomes a complex and dynamic challenge. This task becomes particularly critical when considering the symptomatic variations between different strains over time and their impact on disease characterization, especially since symptomatology often remains the primary basis for diagnosis. In this context, over the past few decades, Machine Learning (ML) algorithms have emerged as powerful analytical tools. By identifying patterns in complex datasets, ML techniques play a crucial role in recognizing the disease across its diverse manifestations, thereby contributing to more accurate diagnostic outcomes. Thus, this study proposes PandemAI, a data-driven framework designed to analyze how symptom variations driven by the evolution of viral variants affect disease recognition over time. The framework comprises three phases: exploration of symptom patterns, symptom rule mining, and symptom-based diagnostic prediction. To validate the proposed approach, we employ data from the Brazilian Severe Acute Respiratory Syndrome (SARS) surveillance system.Doctoral Thesis INHABS: INteroperable Hierarchical Architecture using Blockchain for Smart Cities(Universidade Federal do Rio Grande do Norte, 2025-05-29) Loss, Stefano Momo; Cacho, Nélio Alessandro Azevedo; Lopes, Frederico Araújo da Silva; http://lattes.cnpq.br/9177823996895375; http://lattes.cnpq.br/4635320220484649; https://orcid.org/0000-0003-0093-4930; http://lattes.cnpq.br/7177455143291752; Zorzo, Avelino Francisco; http://lattes.cnpq.br/4315350764773182; Nakagawa, Elisa Yumi; https://orcid.org/0000-0002-7754-4298; http://lattes.cnpq.br/7494142007764616; Cavalcante, Everton Ranielly de Sousa; https://orcid.org/0000-0002-2475-5075; http://lattes.cnpq.br/5065548216266121; Batista, Thais Vasconcelos; https://orcid.org/0000-0003-3558-1450; http://lattes.cnpq.br/5521922960404236Background: Urbanization is rapidly increasing worldwide, necessitating the development of innovative solutions for managing smart city systems. These systems are typically heterogeneous and independently managed, often leading to interoperability challenges across different administrative levels, including local, regional, and national domains. Current approaches, such as middleware or ad hoc integrations, fail to ensure scalability, reliability, and unified data exchange. Blockchain technology has emerged as a potential solution for enabling trusted and decentralized interoperability, yet its full potential in achieving unified system integration remains underexplored. Objective: This thesis aims to design, implement, and evaluate a hierarchical blockchainbased architecture: INHABS (Interoperable Hierarchical Architecture using Blockchain for Smart Cities), to enable reliable, secure, and scalable system interoperability for smart cities. The study addresses gaps in existing solutions by focusing on multi-level integration and creating new functionalities arising from interoperation across diverse domains. Method: Using the Design Science Research (DSR) methodology, the study combines literature reviews, case studies, and iterative artifact development. Initial approaches include blockchain-based microservices and middleware for system integration. These were evaluated to identify limitations, which informed the development of a novel hierarchical blockchain architecture capable of supporting multi-level governance in smart cities. Experimental validation was conducted using performance metrics such as scalability, latency, and throughput. Results: The research developed the INHABS architecture, which successfully integrated smart city systems across hierarchical levels. The framework ensures secure data exchange, reliable interoperability, and scalability, meeting unified system integration requirements. Case studies involving integrating computer-aided dispatch (CAD) systems and global vaccination management demonstrated its effectiveness in optimizing resources, enhancing urban service delivery, and securely sharing sensitive public health data. Conclusions: The INHABS architecture advances the state of the art in smart city interoperability by providing a scalable and secure blockchain-based solution for multi-level integration. The framework enables unified functionality across diverse systems, improving urban service delivery and citizen quality of life by overcoming the limitations of existing microservice and middleware solutions. Future research could expand the application of this architecture to additional use cases and enhance its scalability for more complex urban environments.Master Thesis Utilizando aprendizado de máquina na identificação de Null pointer exceptions em análise estática de código em Java(Universidade Federal do Rio Grande do Norte, 2023-01-30) Silva, Rodrigo Lafayette da; Cavalcante, Everton Ranielly de Sousa; Araújo, Daniel Sabino Amorim de; https://orcid.org/0000-0001-5572-0505; http://lattes.cnpq.br/4744754780165354; https://orcid.org/0000-0002-2475-5075; http://lattes.cnpq.br/5065548216266121; http://lattes.cnpq.br/3567167027912233; Abreu, Marjory Cristiany da Costa; https://orcid.org/0000-0001-7461-7570; http://lattes.cnpq.br/2234040548103596; Almeida, Rodrigo Bonifácio de; https://orcid.org/0000-0002-2380-2829; http://lattes.cnpq.br/0368311142108150Mainstream object-oriented programming languages admit null values for references for the sake of flexibility. In Java, attempting to use an object reference with a null value throws a Null Pointer Exception (NPE), one of the most frequent causes of crashes in Java applications. Static analysis has been used to inspect the source or binary code to locate the origin of the exception by analyzing these artifacts without debugging-oriented program executions. Despite its effectiveness, static analysis relies on a fixed, static set of rules describing violation patterns, and it is known for a significant number of false positives. This study investigates how the use of Machine Learning (ML) techniques can improve the precision of detecting NPE-related faults through static analysis, a branch still unexplored in the literature and the software industry. The main goal is to propose, implement, and evaluate a classification-based approach to address the detection of NPErelated faults in Java code. The expected contributions from this work are: (i) understanding how ML techniques can be used to detect those faults via static analysis; (ii) providing a ML model to detect NPE-related faults; and (iii) an assessment of the performance of ML techniques in comparison to traditional static analysis tools. The results of the experiments showed that the new approach using Machine Learning (k-Nearest Neighbors) is more effective than traditional SATs, namely PMD, SpotBugs, SonarLint, and Infer, regarding NPE detection, presenting an average accuracy of 97.5% in its best configuration, albeit being up to 15 times less efficient in terms of relative performance.