Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE)
Este documento traz o Prefácio e a Organização do CIbSE 2025.
Using Real-world Scenarios to Bridge Training and Practice in Evidence-Based Software Engineering
Context: Training is crucial for the adoption of Evidence-Based Software Engineering (EBSE), but there is a gap in its integration with professional practice. Objective: To investigate the creation and use of scenarios that enhance EBSE training by simulating the application of evidence in real-world situations. Method: A mixed methods study was conducted, starting with consultations with three Uruguayan industry and government practitioners to create a practical scenario, followed by its use to improve an EBSE course. Results: Scenario development is challenging, primarily due to limitations of evidence, but their use enhances students’ ability to apply evidence. Conclusions: Using scenarios to align EBSE training with practice seems to be feasible & effective.
Integración de herramienta para el seguimiento ágil mediante el uso de Machine Learning
El enfoque ágil ha impulsado la necesidad de herramientas avanzadas para la gestión de proyectos, especialmente en entornos multiproyecto. Este trabajo presenta la integración de un modelo de Machine Learning (ML) en Worki, una plataforma de seguimiento ágil, para analizar datos históricos de Sprints y evaluar el rendimiento. El modelo clasifica y evalúa el progreso y ritmo de trabajo, mostrando los resultados directamente en la plataforma. Esto permite una visión basada en datos, facilitando la detección temprana de anomalías y mejorando la eficiencia en la gestión de proyectos.
Integrating Participatory Design and Dual-track Software Development Process: A Case Study From an Intelligent Mathematics Tutoring System
The demand for educational technology has increased research into Intelligent Tutoring Systems (ITS) for low-connectivity regions. However, misalignment between requirement discovery and software development hinders adoption. This case study examines the development of an unplugged ITS using dual-track agile development and participatory design to ensure continuous alignment with pedagogical needs. This study contributes to software engineering by demonstrating how we integrated dual-track development and participatory design to enhance agility while maintaining a user-centered approach in a project with a scalable model for ITS applications in low-resource environments.
Evaluación de la calidad de historias de usuario usando modelos de lenguaje de gran tamaño: un estudio en la industria
La especificación y mantenimiento de historias de usuario de alta calidad son actividades críticas y desafiantes en el desarrollo de software ágil, debido a la naturaleza dinámica de los proyectos, la ambigüedad del lenguaje natural y el esfuerzo requerido para las evaluaciones manuales. Este estudio investiga el uso de modelos de lenguaje de gran tamaño (LLM) para evaluar la calidad de historias de usuario en un proyecto de software de industria, utilizando los criterios del marco INVEST. Se compara el desempeño de tres herramientas LLM con dos evaluaciones de personas expertas en ingeniería de requisitos. Los resultados indican que los LLM tienen potencial para apoyar la evaluación automatizada de historias basado en INVEST.
Modelo de Características Extendido para Sistemas Híbridos (Cuántico-Clásicos)
La computación cuántica (CQ) y la ingeniería de software cuántico (ISC), enfrentan desafíos debido a la diversidad de algoritmos, lenguajes y estrategias de integración. Este trabajo propone un modelo para el desarrollo de software híbrido cuántico-clásico, siguiendo un enfoque de Líneas de Producto de Software. A partir de un modelo previo y una búsqueda de la literatura, se identificaron componentes esenciales y se estructuraron en una jerarquía para representar la variabilidad en estos sistemas. El modelo resultante facilita la gestión de variabilidad y configuración de arquitecturas híbridas, estableciendo las bases para futuras evaluaciones y refinamientos, contribuyendo a una metodología más efectiva para la integración de CQ e ISC.
Exploring the Role of Women in Hugging Face Organizations
Background: Despite its impact on innovation, gender diversity remains far from fully being achieved in open-source projects. Aims: We examine gender diversity in Hugging Face (HF) organizations, investigating its impact on innovation and team dynamics in open-source development projects. Method: We conducted a repository mining study, focusing on ML model development projects on HF, to explore the involvement of women in collaborative processes. Results: Women are highly underrepresented in both organizations and commits distribution, which is also found when analyzing individual developers. Conclusions: Addressing gender disparities is essential to create more equitable, diverse, and inclusive open-source ecosystems.
Generación de Pruebas Unitarias con LLMs en Entornos Industriales: Desafíos, Evolución y Lecciones Prácticas
Los Modelos de Lenguaje de Gran Escala (LLMs) muestran potencial para generar pruebas unitarias automáticamente, pero su aplicación industrial genera desafíos. Presentamos un caso de estudio longitudinal sobre la implementacion y evaluación de LLMs para generación de pruebas en la empresa LKS Next, integrando herramientas estandar como SonarQube. Nuestro enfoque revela hallazgos sobre la evolucion temporal de estas tecnologías en entornos de producción y proporciona lecciones aprendidas. Los resultados ofrecen una guía industrial basada en evidencia para organizaciones que consideran adoptar estas soluciones, destacando consideraciones practicas de integración y mantenibilidad a menudo ausentes en estudios teoricos.
Viagem à Lua - RPG aplicado ao ensino de Testes de Software com elementos do Design Socialmente Consciente
Este artigo apresenta o uso de um framework para criação de RPG (Role-playing Game), aplicado ao ensino de Engenharia de Software (ES), para estimular a participação ativa e ampliar o protagonismo dos alunos no processo de ensino e aprendizagem. O framework, chamado CreativEduc, possui elementos básicos para criação de RPG somados aos artefatos do Design Socialmente Consciente (DSC), que possibilita a obtenção de uma visão sistêmica e social do problema. Foi criado um RPG para a disciplina Teste de Software de um curso de graduação de ES. Relata-se o planejamento, execução e avaliação do jogo e do framework. Foram aplicados questionários resultando em indícios positivos ao seu uso e aplicação.
A Method for Regression Testing Plan Ordering for Non-Automated Executions in Black Box Testing
In this paper, we propose a method for prioritizing regression test cases based on the probability of detecting software execution failures without source code analysis. To achieve this, our method employs the SentenceBERT model to extract embeddings from textual information of development commits and test scripts. These embeddings are then used by machine learning models to predict the probability of detecting a failure. Our experiments show that the proposed method achieves results equal to or better than those of human experts in 92.52% to 94.24% of scenarios when evaluating the APFD (Average Percentage Faults Detected) metric, an overall gain of 10% in APFD mean and a potential gain of up to 6.03% in test plan prioritization counting cases.
A Decade of Turnover Intention Research in IT: Key Insights
Employee turnover intention poses challenges for IT organizations, affecting performance and strategic goals. Despite research efforts, the factors driving turnover remain fragmented. This study conducted a systematic literature review of research published between 2015 and 2024, using PRISMA guidelines. A total of 41 studies were analyzed, identifying 29 internal and 25 external factors. Job satisfaction was the most cited internal factor, while organizational commitment was the most frequent external factor. Burnout also emerged as a key driver. These findings support IT organizations in developing targeted HR strategies to improve retention.
An Agent-Oriented Model-Driven Development Process for Cyber-Physical Systems
Cyber-Physical Systems (CPS) integrate distributed software and hardware, requiring systematic engineering approaches. This paper presents a Model-Driven Development (MDD) process that spans the entire CPS lifecycle, from conceptual modeling (CIM) to functional implementation (Code). By leveraging agent orientation, the approach simplifies system structuring and enables semi-automated transformations through a domain-specific language (DSL). A proof of concept validates the process in a greenhouse automation scenario, demonstrating that the generated software functions as expected on real hardware. The results confirm the feasibility of this end-to-end MDD workflow for CPS development.
Factores Personales que Influyen en la Realización de Tareas bajo Test-Driven Development: Meta-Análisis de una Familia de Experimentos
Antecedentes: La experimentación en Ingeniería del Software requiere frecuentemente que los sujetos experimentales realicen algún tipo de tarea. Objetivo: Estudiar el grado en que se realizan las tareas experimentales, en función de los aspectos instrumentales de un experimento y de las características personales de los sujetos. Método: Meta-análisis de una familia de experimentos en Test-Driven Development. Resultados: Los sujetos de mayor edad y más experiencia profesional realizan menos trabajo en las tareas experimentales. La tarea experimental puede influir en el grado de participación en el experimento. Conclusiones: Deben estudiarse las motivaciones de los sujetos experimentales y establecer mecanismos de control que aseguren un correcto desempeño.
Modelo de referencia de ciberseguridad para prevenir ataques de red a infraestructuras críticas en la era cuántica
Este documento presenta un modelo de referencia de ciberseguridad para proteger sistemas ciber-físicos (CPS) en infraestructuras críticas (IC) ante las amenazas de la computación cuántica. La revisión sistemática de la literatura (RSL) revela la urgencia de incorporar criptografía post-cuántica y de abordar retos como la interoperabilidad con sistemas heredados y los altos costos de implementación. MoRCiTO integra controles de acceso, monitorización continua y métodos de detección y mitigación de ciberataques de red, desarrollado mediante investigación-acción en tres ciclos.
Hacia una arquitectura de referencia para la definición de arquitecturas de software empresariales híbridas, adaptada a las necesidades emergentes de la Ingeniería de Software Cuántica
El auge de la computación cuántica ha abierto un nuevo paradigma en el procesamiento de la información, prometiendo superar los límites de la computación clásica. Sin embargo, el desarrollo de arquitecturas de software que puedan explotar plenamente este potencial es aún un campo de estudio constante, lleno de desafíos sin precedentes. Este artículo presenta el estado actual de la propuesta de doctorado que se centra en el diseño de una arquitectura de referencia que apoye la definición de arquitecturas de sistemas software híbridos, atendiendo a las necesidades y desafíos que propone el avance en el campo de la Ingeniería de Software Cuántico.
Detección de Patologías Pulmonares: Un Enfoque Basado en IA con Redes Neuronales Convolucionales
El diagnóstico temprano de enfermedades pulmonares mejora significativamente la supervivencia. Este estudio utiliza un enfoque de investigación-acción con CNNs, ResNet152 y Vision Transformers, optimizados con Data Augmentation. Aplicando la norma ISO 25059:2023, se abordan la interpretabilidad, robustez y seguridad de los datos. Los resultados iniciales muestran una precisión superior al 93%, apoyada por mapas de calor que destacan zonas clave en las imágenes, facilitando la labor médica. Se integran principios éticos para proteger la privacidad del paciente. El proyecto busca reducir errores diagnósticos y promover el uso seguro de inteligencia artificial en hospitales, con miras a validación clínica futura.
Identificación Ágil de Microservicios Utilizando DDD y BDD
La adopción de microservicios mejora la mantenibilidad y escalabilidad frente a arquitecturas monolíticas, aunque su diseño inicial es desafiante. Esta investigación propone un marco ágil que integra Desarrollo Guiado por Comportamiento y Diseño Guiado por el Dominio para identificar microservicios y definir arquitecturas escalables. Un análisis de la literatura y un experimento evidencian que combinar diseño estratégico y prácticas ágiles reduce la deuda técnica y mejora la escalabilidad.
Explorando el Uso de Inteligencia Artificial en el Descubrimiento y Diseño de Microservicios
El diseño de microservicios es un desafío significativo en desarrollos desde cero, especialmente para garantizar cohesión interna y bajo acoplamiento. Este trabajo propone un enfoque basado en inteligencia artificial, particularmente en procesamiento de lenguaje natural (PLN) y aprendizaje profundo, para apoyar el descubrimiento y diseño de microservicios a partir de requisitos textuales. Utilizando la metodología de Design Science Research (DSR), la propuesta se centra en técnicas de ingeniería de prompts y validación arquitectónica para guiar y evaluar la calidad de diseños orientados a microservicios. Como parte de un proyecto doctoral, esta investigación busca optimizar la calidad arquitectónica, el tiempo de diseño y los costos, abordando retos y oportunidades identificados mediante un mapeo sistemático de la literatura (MSL) y una revisión sistemática de la literatura (SLR).
Towards a process for Trustworthy AI systems development
This work aims to define a comprehensive Software Development Life Cycle (SDLC) tailored for Trustworthy Artificial Intelligence (AI) systems. These systems are understood as full software solutions that incorporate AI components— such as machine learning models or intelligent agents—whose behavior must align with ethical principles, legal requirements, and technical robustness. The proposed SDLC is grounded on a multidimensional taxonomy of trustworthiness, covering aspects such as lawfulness, non-maleficence, beneficence, autonomy, justice, explicability, and technology. By integrating these principles into all development phases, the SDLC supports the design of AI systems that are not only effective and innovative but also aligned with human values, regulations, and societal expectations. The methodology follows a Design Science Research approach, ensuring the model’s relevance, feasibility, and adaptability to evolving technological and regulatory contexts.
Hacia el desarrollo de un modelo adaptativo para gestionar la deuda social en entornos de desarrollo de software ágil distribuido
La desalineación de la congruencia sociotécnica en equipos de desarrollo de software, entendida como la desarticulación entre factores sociales y técnicos, puede generar decisiones subóptimas que conllevan a costos imprevistos dando origen al concepto de deuda social. Mediante una revisión sistemática de la literatura, se identificaron soluciones para detectar la deuda social; sin embargo, estas propuestas operan de forma independiente. Por ello, se propone el desarrollo de un modelo adaptativo para gestionarla en equipos ágiles distribuidos, integrando causas, efectos y estrategias. El modelo se sustenta en un enfoque de Investigación-Acción estructurado en cuatro ciclos.
IoT Security Evaluation: A Set of Strategies Combined of Inspection, Testing, and Experimentation
IoT software systems research has significantly expanded recently, particularly with AI technologies' emergence and widespread adoption. Given their critical nature of continuous connectivity, such systems demand robust security measures. Therefore, it is fundamental to apply technologies that support their quality assurance (QA), specifically security. This thesis proposes strategies combining quality assurance technologies (software inspection, testing, and continuous experimentation) to enhance the final quality of these systems.
Las estrategias de retención de talento en la transformación digital y su impacto en la rotación de personal de tecnología
La rotación de personal en el sector de Tecnología de la Información (TI) es un desafío clave en la transformación digital. Este trabajo doctoral investiga la relación entre estrategias de retención y motivos de rotación en empresas de TI en Brasil. La hipótesis plantea una relación significativa entre dichas estrategias y la permanencia o salida de los profesionales. La metodología combina entrevistas cualitativas y análisis temático, junto a una simulación de sistemas multiagente (SMA) en GAMA para evaluar factores clave en la rotación. Los hallazgos preliminares reflejan que, pese a las estrategias aplicadas, la rotación sigue siendo un reto, especialmente ante la alta competitividad y la evolución tecnológica. Este estudio contribuye al desarrollo de políticas de recursos humanos más eficaces.
Insights into API Management Quality Characterization, Measurement and Evaluation
APIs have become a cornerstone in software ecosystems: organizations have been increasingly connecting software applications to share complex digital assets. However, managing APIs is non-trivial, and companies have been struggling with different quality-related issues in managing their APIs. The main goal of this work is to create a framework that allows characterizing, assessing, and improving API Management and API governance, particularly focusing on API Gateways as the core elements of API Management ecosystems.
Impulsando el pensamiento crítico de los jefes de proyecto IT en el proceso de adopción de la GenAI
La investigación se centra en los jefes de proyecto IT, analizando su habilidad de pensamiento crítico y su comportamiento en la toma de decisiones complejas. El objetivo principal es proporcionar herramientas para fortalecer esta competencia, que podría convertirse en un valor diferencial clave para estos profesionales en el proceso de adopción de la IA Generativa (GenAI).
Model-Driven Software Engineering to Foster the Adoption of Digital Twins
Digital twins are virtual replicas of physical systems that offer significant benefits in terms of productivity and sustainability. However, most digital twin solutions are tailored to specific use cases, and there is a lack of domain-independent development frameworks. As a result, developers must build each system from scratch, which increases project timelines and costs and hinders the scalability of solutions. This ongoing doctoral project proposes a Model-Driven Software Engineering (MDSE) approach to simplify the development of digital twins. Ultimately, the presented approach aims to promote the widespread adoption of digital twins across sectors, making their development more accessible, scalable, and cost-effective.
Desarrollo y Evaluación de un Tutor Inteligente para el aprendizaje de programación basado en los Modelos de Lenguaje Extenso
El comportamiento emergente de la programación automática tras la popularización de la Inteligencia Artificial Generativa ha generado incertidumbre sobre el futuro de la programación y su enseñanza. Este trabajo doctoral propone diseñar y evaluar una arquitectura para el desarrollo de Sistemas de Tutoría Inteligente para el aprendizaje de programación que integran Modelos de Lenguaje Extenso para ofrecer una experiencia de usuario personalizada. La arquitectura se desarrolla con una metodología de investigación basada en diseño, evaluándose su efecto en el compromiso cognitivo y el aprendizaje mediante pruebas formativas del prototipo y una evaluación sumativa a través de un estudio de intervención.
Mitigation of Cognitive Biases in Innovative Software Engineering Projects: A Proposal
Cognitive biases are critical in software engineering (SE) affecting replanning, failures and even cancellations. This work aims to identify and mitigate cognitive biases in the management of innovative projects in SE, in a large multinational company. Using the Design Science Research (DSR) method, a (1) Systematic Literature Review (SLR) will be carried out, (2) complementary research, (3) study of technological alternatives and (4) application of an artifact in the company’s project portfolio to identify the behavior of the five most impactful biases in SE, proposing a framework with practical recommendations to eliminate them, contributing to future research in the industry.
Model-Driven Engineering for Implementation and Testing of Large Language Model Architectures
Large Language Model (LLM) architectures such as DeepSeek-V3 demonstrate reductions in computational costs through the design of efficient architectures, but their development reveals technical debt in the software engineering principles associated with LLM development, partly caused by the multidisciplinary nature of the field. Consequently, it leads to increased development costs and challenges in software quality. To address these issues, this thesis proposes the integration of Model-Driven Engineering into the LLM development life cycle. A conceptual metamodel formalises LLM architectural constructs (RQ1), enabling automated code generation via model transformations (RQ2) and facilitating Model-Based testing (RQ3).
Engenharia de Requisitos de Explicabilidade em Sistemas Baseados em Aprendizado de Máquina
A explicabilidade e a habilidade de tornar as previsões de um modelo de Aprendizado de Maquina (AM) claras e compreensíveis para as pessoas. A adoção de modelos de AM em decisões críticas exige que a explicabilidade seja incorporada como um requisito fundamental no processo de tomada de decisão. Por ser um tema recente, a explicabilidade em sistemas baseados em AM ainda carece de processos sistematizados, o que abre espaço para novas pesquisas. Este trabalho de doutorado visa desenvolver e contribuir com uma abordagem de Engenharia de Requisitos para analise e especificação de requisitos de explicabilidade em sistemas baseados em AM. A metodologia Design Science servirá de modelo para conduzir esta pesquisa.
A Model for Non-Intrusive Capture of Metrics for Early Project Estimation in Agile Environments
Early estimation in agile projects is essential for determining feasibility, costs, and planning. However, lacking detailed initial requirements introduces uncertainty and leads to inconsistent results. Although the literature proposes predictive models based on historical data to improve accuracy, the lack of databases tailored to each company’s context prevents practitioners from applying these models effectively. This work presents a non-intrusive model for capturing key metrics (such as software size) in agile environments to facilitate the creation of historical databases. The model will be calibrated and validated through empirical studies to support the gradual adoption of a hybrid estimation approach that combines data-driven techniques with expert judgment. Preliminary results indicate that the scarcity of historical data in practice reduces estimation accuracy, highlighting the need for automated metric capture. This approach aims to enhance the reliability of early estimations in agile projects and provide objective support for decision-making in software project management.
Minería de procesos e inteligencia artificial para el diseño, sostenibilidad y mejora de procesos colaborativos
En las últimas décadas, la minería de procesos ha sido clave para mejorar procesos en las organizaciones. Hoy, la complejidad creciente, con procesos colaborativos que involucran múltiples actores, exige nuevas perspectivas de diseño y mejora. Al mismo tiempo, la sostenibilidad y la inteligencia artificial (IA) han cobrado protagonismo. Este trabajo doctoral busca ampliar el alcance de la minería de procesos y la IA, integrando técnicas, prácticas y herramientas para el diseño, sostenibilidad y mejora de procesos colaborativos.
Plataforma para la Gestión de Datos Espaciales y su Aplicación en Gestión en el área Salud
Los datos espaciales son generados por diferentes organismos. Las organizaciones de salud tienen datos propios que pueden ser espacializados. Usar estos datos en conjunto permite realizar analisis espaciales para apoyar en la gestion y planificación de los servicios de salud. Sin embargo, el análisis conjunto de dichos datos presenta diferentes desafíos. Este trabajo busca proponer mecanismos que faciliten la gestion conjunta de datos, para brindar soporte a la evaluacion de acceso y planificación de los servicios de salud. En particular, se propone una plataforma, construida siguiendo el enfoque Design Science Research, en forma iterativa para generar el diseno e implementacion. Se evaluara mediante instancias de intercambio con expertos en salud y técnicos.
Integrating ISO/IEC 29110 into Agile Workflows: A Practical Intervention Strategy for Very Small Entities
Very Small Entities (VSEs), crucial to the software industry, often struggle to balance agile practices with the need for standardized processes to ensure quality. This research addresses the challenge of adopting ISO/IEC 29110 in agile VSEs, where resource constraints and a preference for informality hinder standardization. We propose a multi-faceted intervention strategy focusing on gradual, low-intrusion adoption of ISO/IEC 29110, leveraging existing tools and aligning with agile principles. The methodology mapping, case studies, and professional surveys to develop and validate this strategy. The expected outcome is an implementation strategy, supported by tools and instruments, to enhance both process and product quality in VSEs.
Multi-View Spectral Clustering for Monolith-to-Microservices Migration
The migration from monolithic architectures to microservices poses significant challenges, particularly in achieving modularity, decoupling and functional coherence. This paper explores a multi-view spectral clustering approach to systematically decompose monoliths by integrating structural dependencies, domain semantics and dynamic behavior into a unified weighted affinity model. The method uncovers optimal service boundaries that balance cohesion, coupling and complexity, generating multiple architectural options tailored to weighted quality attributes. Validation on a real-world application demonstrates how the framework enables flexible, quality-driven decompositions, supporting more informed and adaptive architectural decisions.
Arquitectura de un videojuego serio de RV para obtener estrategias de educación personalizadas para el desarrollo de habilidades cognitivas específicas
El aprendizaje matemático es crucial, y las habilidades cognitivas son clave para impulsarlo. En México, las deficiencias en matemáticas desde educación básica representan un reto educativo. Este artículo presenta una arquitectura escalable y modular de un videojuego serio de realidad virtual, basada en MVC, para fomentar el aprendizaje matemático y el desarrollo cognitivo personalizado. Aplicando una metodología híbrida (Design Thinking, ADDIE y el Proceso de Desarrollo de Videojuegos), la propuesta es una solución educativa que usa algoritmos de personalización, además del estilo de vida y desempeño cognitivo del usuario, para adaptar un plan de estrategias que potencie sus habilidades cognitivas y aprendizaje matemático.
Alianza Europea de Innovación para la Educación del Testing - Proyecto ENACTEST
La importancia de las pruebas de software es indiscutible, pero su falta de implementación adecuada puede provocar aplicaciones con errores y problemas. Esta situación se debe a un desajuste entre las habilidades demandadas por la industria, las necesidades de aprendizaje de los estudiantes y los métodos actuales que se utilizan para la enseñanza de las pruebas en instituciones educativas. El proyecto ENACTEST busca cubrir este desajuste mediante el desarrollo de cápsulas de enseñanza para las pruebas del software (testing) adaptadas a las demandas de la industria y a los procesos cognitivos de los estudiantes. El consorcio incluye universidades, centros de formación profesional y pequeñas empresas, que representa un entorno socioeconómico amplio.
Building a Smart City that Stimulates Innovation Using Open Source and Decentralised Technologies
We are developing Santa Rosa municipality as a smart and sustainable region that stimulates innovation. A place that will take advantage of the data–driven economy and data–centric applications with Equality, Diversity and Inclusion (EDI) as a priority. To this end, have built a pilot smart city lab for testing technologies in real-life conditions; we are currently exploring decentralised technologies and technologies with low environmental cost. Also, we are following competency frameworks for smart cities rather than deploying technologies indiscriminately. 12 years of work have produced tangible results and practical experience that we share in this paper.
Development of San Marcos as a Smart and Sustainable City with the Support of Search-based Software Engineering
The authorities of San Marcos' municipality (Mexico) have funded a project to develop it as a smart and sustainable place. This requires funding, technology, and expertise that they scarcely have, since they are afflicted by digital exclusion. To face the challenge, they have teamed up with Unijuí University and Santa Rosa city (Brazil) under Memoranda of Understanding (MOU) and with University of Cambridge under academic collaboration. In this paper, we (the team) discuss the main technical problems, technology used, results achieved and pending. Our strategy is to use low-cost technologies with optimised deployments to minimise maintenance and environmental costs.
Automated Unobtrusive Techniques for Linking requirements and testing in agile software development AUTOLINK PROJECT
Software professionals often face challenges in defining requirements and conducting thorough testing, particularly within agile development environments. The rapid and frequent iterations of agile methods encourage developers to take shortcuts to meet tight release deadlines, which may lead to low-quality software. To enhance software quality, the AUTOLINK project explores the synergies between requirements engineering and software testing, aiming to seamlessly integrate them into agile environments. AUTOLINK seeks to develop techniques that enable practitioners to improve software quality by testing it against its requirements in a way that is unobtrusive, cost-effective, and requires minimal effort.
Comparing LLMs in business rule-following
Large Language Models (LLMs) have shown great capabilities in language understanding and instruction following. However, to the author’s knowledge, no prior work has evaluated their performance in real industry internal rule-following scenarios compared to humans. The present R&D project aims to analyze the applicability of LLMs in improving efficiency in task analysis and scheduling through automatic team assignment, following a set of internal business rules. The study was funded by SUFRAMA and is a collaboration between INDT and Motorola Mobility. The experiment results show that lightweight open LLMs, on average, have worse accuracy than mean worker (57.5% x 86.25%) with a higher divergence rate (90% x 45%).
Test Case Creation Approach using LLM for Android System
This research project presents a method to generate functional test cases for new Android features using the Large Language Model (LLM) LLama. The objective is to reduce manual effort and avoid process bottlenecks, benefiting software testing teams and developers. The approach uses LLM-generated prompts based on Android feature specifications for automated test case generation. A feasibility study evaluated 57 test cases, revealing that 19.3% were well-written, 63.2% had acceptable quality, and 26.3% had medium complexity. The research was conducted under contract with the Institute of Technological Development (INDT) and its funded by the R&D program of EMBRAPII for Motorola LTDA/Flextronic Ltda.
Music360: Software Engineering for Assessing the True Value of Music
While it is well known that music influences our emotions, health and work, there is a lack of standardised information to measure and share the true impact of music and how this relates to the distribution of royalties. The Music360 project, led by a European consortium, aims to assess the value of music and connect stakeholders across the music value chain to build a common data analytics platform that goes beyond traditional metrics to explore the impact of music. The Music360 platform is based on a novel model-driven approach that will be applied to empirical case studies in the form of living labs in healthcare, media, retail, etc. to evaluate how music enhances well-being, patient recovery, brand identity and revenue potential.
Quality concerns when integrating quantum and classical computing: a systematic mapping study
This article presents a systematic mapping review to categorize software quality research for hybrid computing systems; meaning the combination of classical software and quantum technologies to solve a problem. This work attempts to categorize primary studies with respect to quality attributes and metrics in hybrid systems reported in state of the art literature. Moreover, it identifies potential research areas and surveys the challenges of the practical integration of quantum software into classical infrastructures.
Towards a Software Toolkit for Specifying and Monitoring Smart Contracts in the Application Integration Domain
An API for Developing Secure Integration Processes
Este artigo não possui um resumo.
¿Cómo perciben los ingenieros de software su productividad trabajando de forma remota?
El trabajo remoto se afianzó tras la pandemia de COVID-19. Mientras grandes empresas buscan el retorno a oficinas, este estudio analiza la percepción de ingenieros de software en México, España, Colombia y Argentina sobre el teletrabajo.
Unraveling Software Antipatterns and Smells Definitions
Software technical debt may persist if the root cause is misidentified. We argue that this stems from misconceptions about antipatterns and smells. This article reviews the literature to clarify their similarities and differences. Findings suggest that both terms are used interchangeably, with “smell” being more common, though this ambiguity may affect optimal solutions in software projects.
Gestión de autorización de acceso a microservicios en sistemas de gestión pública: una propuesta de automatización
This study presents SIARE-Artefactos, to automate the registration of access authorization to resources. It is designed with microservices, Java Spring Boot Starter, and OAuth 2.0 integrated into CI/CD processes. The results demonstrate a 60% reduction in configuration time and enhanced system reliability.
Preliminary Results of a Systematic Mapping Study of AIOps Practices and Trends
This work aims at identifying the main AIOps techniques reported in peer-reviewed literature in the last six years (1) and classify the techniques according to what software quality attribute to they contribute to. It presents the preliminary results of a systematic mapping study considering peer-reviewed literature. 72 peer-reviewed studies were analyzed in order to extract strategies and practices. Anomaly detection and fault prediction were the strategies most often applied on AIOps. Classical machine learning techniques, as well as deep learning techniques were the most frequently found groups of techniques in literature. Availability and maintainability are the most frequently software quality attributes improved by applying AIOps techniques.
Personality, Security, and User Satisfaction in Context-Aware Applications
This study explores how personality traits influence the perception of security and satisfaction of context-aware applications. Through a web-based survey using animated videos, we find that openness and agreeableness influence security perceptions, while satisfaction is more context-dependent. A positive correlation was also found between perceived security and satisfaction.
Using Gamification to Teach Security Analysis in Higher Education
Teaching security analysis at university allows future professionals to acquire with the necessary skills to protect and maintain the integrity of digital infrastructures. This work presents a board game and its practical use for teaching security analysis in 1st-year bachelor students. The game has been employed in the past four years and our perception of use is positive. Being a game with a simple and most-likely known mechanics, it could be adapted and used by other educators to teach software engineering topics.
A Non-Intrusive Model for Capturing Metrics for Early Project Estimation in Agile Environments
Early estimation is crucial in agile projects, but a lack of detailed initial requirements often makes it inaccurate. While data-driven models improve accuracy, the absence of company-specific databases hinders the reliance on historical data. This work presents a non-intrusive model for capturing key metrics (e.g., software size) in agile settings to facilitate building these databases. The model aims to support a hybrid estimation approach that combines data-driven techniques with expert judgment. Preliminary results highlight the need for automated metric capture to enhance the reliability of early agile estimations.
Challenges in adopting ISO/IEC 29110 standardized processes in Agile VSEs
Very small software entities (VSEs) face challenges when attempting to incorporate structured quality practices into their agile environment. The ISO/IEC 29110 standard aims to address these challenges; however, its adoption still faces some issues. This study identifies adoption barriers through an ongoing systematic mapping aiming to prioritize those with the highest impact on standard adoption. Preliminary results highlight organizational barriers, technical challenges, and psychosocial factors. These findings lay the groundwork for future research to compare factors with case studies, aiming to develop an adaptive framework that closes the gap between agility and standardization, enabling VSEs to adopt critical disciplines of the standard.