Examinando por Autor "Hidalgo Barrientos, Mauricio Fernando"

Mostrando 1 - 6 de 6
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    A study of software architects’ cognitive approaches: kolb’s learning styles inventory in action
    (Springer, 2024-10-19) Hidalgo Barrientos, Mauricio Fernando; Astudillo, Hernán; Castro, Laura M.
    The multidisciplinary nature of software architects demands a diverse set of skills, ranging from technical expertise to interpersonal abilities. Within this domain, software architects are responsible for designing systems that adhere to quality standards, meet functional requirements, and align with organizational goals. However, educating or training software architects presents a challenge due to the complexity of their roles and responsibilities. To address this challenge, this paper proposes an approach to understanding the learning styles of software architects using Kolb’s Learning Style Inventory (KLSI). It aims to provide a characterization of their teaching and learning preferences, thus facilitating the design and execution of educational strategies tailored to their specific needs. In conducting our research, we utilized LinkedIn as a platform to distribute the KLSI Test, ultimately gathering a sample comprising 18 Senior and Mid-Senior Software Architects. Through trend analysis of their responses, we consistently observed a discernible pattern. This led us to identify the Deciding Learning Style as the primary approach among the sample of software architects regarding their learning preferences. This finding offers initial insights into the predominant learning style within this profession, providing valuable guidance for educational practitioners and institutions aiming to optimize their training programs for software architects.
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    Challenges to applying role playing in software engineering education: a taxonomy derived from a rapid literature review
    (Springer, 2024-07) Hidalgo Barrientos, Mauricio Fernando; Astudillo, Hernán; Castro, Laura M.
    Role Playing (RP) serves as an instructional approach to enrich the learning experience for students and boost their learning by the effective application of their theoretical knowledge within a practical context. In Software Engineering Education (SEE), the utilization of RP proves beneficial in fostering the development of skills such as teamwork, problem-solving, and critical thinking among students and aids them in comprehending the intricacies and hurdles inherent in software development, instilling the significance of collaborative efforts and effective communication. To use role playing effectively, SEE teachers need to understand the challenges that arise from using it. This paper presents a taxonomy, resulting from the analysis of a rapid review developed to identify these challenges: a thorough review of relevant articles indexed by well-known digital libraries (Web of Science, Scopus, and IEEE Xplore) which, after inclusion/exclusion criteria, yielded a total of 23 papers. This taxonomy provides an organized structure for understanding the challenges in implementing Role Playiing (RP) activities in the context of Software Engineering Education (SEE).
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    Deep learning aplicado para la detección de hemorragias y tumores cerebrales
    (Universidade Federal do Paraná, 2021-12-01) Hidalgo Barrientos, Mauricio Fernando; Hayes Ortiz, Bryan Isaac; Delgadillo Vera, Ignacio; Goyo Escalona, Manuel
    Introducción: Un problema que afecta a la salud en Chile se refiere a las patologías cerebrales, toma de exámenes y el alto tiempo de espera para la obtención de los resultados (retrasando el diagnóstico y tratamiento). Actualmente, los exámenes se envían al extranjero para ser procesados y el tiempo de espera juega en contra del paciente. Dada esta realidad, nuestro documento propone un modelo de deep learning para la predicción de imágenes cerebrales que permita obtener un diagnóstico previo, pero no definitivo, en virtud de disminuir el tiempo del proceso y, de ser necesario, priorizar a los pacientes cuya vida estaría potencialmente en riesgo. Métodos: El desarrollo utilizó un enfoque RAD iterativo y las imágenes se recogieron de Kaggle. Adicionalmente, se redimensiona el dataset para normalizar el tamaño y generamos nuevas imágenes utilizando “data augmentation”. Las imágenes fueron procesadas en redes convolucionales, indagando en distintas configuraciones para la red, su optimizador y la función de activación, hasta llegar a un modelo que consideramos razonable. Resultados: Con el modelo definitivo, los resultados superan el 80% de precisión en las predicciones y descubrimos que separar patologías (hemorragias y tumores) fue crucial para este resultado. Conclusiones: Hemos logrado una herramienta de diagnóstico previo, pero se debe continuar la investigación en virtud de aumentar la precisión. Un próximo paso considera ampliar el dataset con imágenes de otras fuentes y separar el modelo para analizar patologías de forma independiente. Motivamos a seguir investigando ya que este tipo de apoyo puede contribuir a salvar vidas.
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    Mapping kolb's learning style to roles in software development team
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-10-08) Hidalgo Barrientos, Mauricio Fernando; Rodriguez, Kattia; Astudillo, Hernán
    The effective transfer and acquisition of necessary knowledge, methods, and attitudes pose significant challenges for Software Engineering Education. Furthermore, training in software development skills and knowledge currently lacks a clear set of techniques to link learning styles and preferences with development team roles. This paper characterizes the learning style of four traditional roles in software development (Analyst, Architect, Developer, and Project Manager) using Kolb's Learning Styles Inventory. Kolb's Learning Styles Test was administered to 110 software development practitioners (15 analysts, 18 architects, 50 developers, and 27 project managers). The test results show that, with some differences, architects and analysts have the Deciding style, while developers and project managers exhibit the Thinking style. Finally, in alignment with Kolb's learning strengths and challenges, this work provides a set of teaching strategies for each role based on their inferred learning styles.
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    Negative sampling for triplet-based loss: improving representation in self-supervised representation learning
    (Springer, 2024-11-17) Manuel Alejandro Goyo; Hidalgo Barrientos, Mauricio Fernando
    Significant strides have been made in artificial neural networks across various fields, necessitating extensive labeled data for effective training. However, the acquisition of such annotated data is both costly and labor-intensive. To address this challenge, Self-Supervised Representation Learning (SSRL) has emerged as a promising solution. One prominent SSRL method, Contrastive Self-Supervised Learning (CSL), enhances feature representations by discerning similarities and differences among samples in the feature space. Yet, accurately identifying dissimilar samples remains a persistent issue, limiting CSL’s effectiveness. In response, an innovative enhancement to CSL is proposed in this paper. Explicit negative sampling strategies using a binary classification algorithm within the feature space are introduced to distinguish between similar and dissimilar features precisely. Additionally, Triplet Loss, originally designed for tasks such as person re-identification and face recognition, is incorporated to further refine feature learning. Experimental evaluations on the CIFAR-10 and SVHN datasets validate the proposed method’s superiority in content-based image retrieval (CBIR) and classification tasks. Significant improvements are demonstrated in metrics such as mean average precision (MAP), accuracy, recall, precision, and F1-score compared to existing techniques. This framework contributes to the advancement of SSRL by enabling scalable neural network training on large datasets with minimal annotation, effectively bridging the gap between supervised and unsupervised learning paradigms.
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    What Is the Process? A Metamodel of the Requirements Elicitation Process Derived from a Systematic Literature Review
    (MDPI, 2024-12-25) Hidalgo Barrientos, Mauricio Fernando; Yanine, Fernando; Paredes, Rodrigo; Frez, Jonathan; Solar, Mauricio
    Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation methods, practitioners face persistent challenges such as capturing tacit knowledge, managing diverse stakeholder needs, and addressing ambiguities in requirements. Moreover, although elicitation is recognized as a core process for gathering and analyzing system objectives, there is a lack of a unified and systematic framework to guide practitioners—especially newcomers—through the activity. To address these challenges, we provide a comprehensive analysis of existing elicitation methods, aiming to contribute to better alignment between software products and project objectives, ultimately improving software engineering practices. We do so by performing a systematic literature review identifying crosscutting steps, common techniques, tools, and approaches that define the core activities of the elicitation process. We synthesize our findings into a metamodel that structures software elicitation processes. This review uncovers various elicitation methods—such as collaborative workshops, interviews, and prototyping—each demonstrating unique strengths in different project contexts. It also highlights significant limitations, including stakeholder misalignment and incomplete requirements capture, which continue to reduce the effectiveness of elicitation processes. Finally, our study seeks to contribute to understanding requirements elicitation methods by providing a comprehensive view of their current strengths and limitations through a metamodel enabling the structuring and optimization of elicitation processes.