Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE)
Este documento traz o Prefácio e a Organização dos Anais do CIbSE 2026.
Teaching Practically Relevant Research Problem Formulation in Software Engineering with Lean Research Inception
[Background] Well-formulated Software Engineering (SE) research problems are essential for bridging the gap between industry-academia. Lean Research Inception (LRI) aims to support this activity. [Goal] Apply LRI to support SE students in formulating practice-aligned research problems. [Method] We conducted a case study with 60 students and 7 faculty advisors of a Brazilian university. [Results] Students reported benefits in reasoning (60%), clarity and definition (61.7%), contextualization (60%), and communication (50%). Advisors also observed clearer and more structured problems (57.1%) with a high recommendation rate (85.7%). [Conclusion] LRI can be a promising approach to support practice-aligned research problem formulation in SE education.
The use of the iStar Framework for modeling tax compliance programs
Modeling tax compliance programs is essential for organizations because these programs translate statutory obligations into organizational controls throughout the tax reporting lifecycle, requiring coordinated responsibilities, evidence generation, and data consistency across departments and thirdparty systems. Although traditional early requirements and process models are commonly used to model tax compliance programs, they are limited in capturing key dependencies among entities such as departments and outsourced systems. To address this gap, this study uses the iStar 2.0 framework as an alternative model to capture these dependencies. This framework focuses on representing actors, goals, and dependencies rather than processes. A proof of concept was conducted in a real-world scenario, where requirements were gathered through interviews and questionnaires and validated using the Delphi technique. The empirical results demonstrate the viability of the iStar framework for modeling and enriching tax compliance programs, highlighting responsibilities and quality attributes such as traceability and auditability. Therefore, the iStar 2.0 framework can be considered for modeling and enriching compliance programs.
Técnicas de Inteligencia Artificial y Aprendizaje Automático para Pruebas Automáticas de Sistemas Conscientes de Contexto: Un Mapeo de Literatura
La automatización de pruebas en sistemas conscientes del contexto se ha convertido en un desafío debido a la creciente complejidad, adaptabilidad y heterogeneidad de estos sistemas. En este escenario, las técnicas de inteligencia artificial y aprendizaje automático han emergido como enfoques clave para apoyar las pruebas de software. Este artículo presenta un mapeo sistemático de la literatura que analiza 73 estudios primarios publicados entre 2009 y marzo de 2025, enfocados en el uso de técnicas de inteligencia artificial y aprendizaje automático para pruebas automáticas en CAS. El estudio caracteriza las técnicas empleadas, los tipos de pruebas abordados, las herramientas utilizadas, los criterios de evaluación considerados y los principales desafíos reportados. Los resultados muestran una predominancia de pruebas de interfaz gráfica y una adopción recurrente de técnicas de aprendizaje automático, en particular redes neuronales profundas y, en trabajos recientes, modelos de lenguaje de gran escala. No obstante, el mapeo evidencia desafíos relacionados con la escalabilidad de las soluciones propuestas, la incorporación de nuevas funcionalidades en las herramientas existentes y la evaluación del rendimiento en escenarios contextuales dinámicos. Como contribución, este trabajo ofrece una visión estructurada del estado del arte sobre el uso de inteligencia artificial en pruebas automáticas de CAS, e identifica oportunidades de investigación futura, especialmente en la integración de técnicas emergentes, como el aprendizaje automático generativo, con potencial para transformar los procesos de prueba y aseguramiento de la calidad del software.
An Empirical Study of LLM-Based Source Code Quality Assessment under ISO/IEC 5055:2021
Nowadays, many software companies are required to meet high quality standards in the development of their applications. Rather than replacing traditional SAST tools based on fixed rules, approaches such as GemCA leverage a Large Language Model (LLM) to perform semantic reasoning over code in relation to the ISO/IEC 5055:2021 standard, providing a complementary decision-support mechanism for early-stage quality assessment. This paper presents GemCA and reports an empirical analysis evaluating its accuracy on a dataset of known code weaknesses. The goal is to examine the framework’s ability to apply the criteria defined by the ISO/IEC 5055:2021 standard. Results show that GemCA achieves an average accuracy of 81% across 15 repetitions, with significantly higher performance in C# than PHP. However, accuracy varies substantially across weakness categories (p = 0.0000), indicating sensitivity to CWE type and programming language. These findings highlight both the potential and the current limitations of LLM-based analysis for ISO/IEC 5055:2021 compliance.
LLM-Assisted INVEST Evaluation and Improvement of User Stories: An Industrial Replication Study
The specification and maintenance of high-quality user stories are critical in agile software development, yet they are often hindered by natural-language ambiguity, evolving business requirements, and the effort required for manual backlog refinement in industrial settings. This paper investigates the use of large language models (LLMs), specifically GPT-5.1, to support the automated evaluation and improvement of user-story quality using the INVEST framework. Building on prior expert-based assessments, we propose a human-in-the-loop procedure that combines LLM automation with requirements-engineering expertise. We conduct an industrial replication study using 49 real user stories from a scholarship‑management system, preserving the evaluation–improvement–reevaluation design of prior expert‑based work. Results show alignment between GPT-5.1 and expert judgments, particularly after an evaluation–improvement–reevaluation cycle, with strong semantic agreement and convergence in key INVEST dimensions. GPT‑5.1 assigns slightly lower scores than experts for Independent, Negotiable, Estimable, and Small, with moderate monotonic correlations (ρ≈0.53–0.65). After the improvement cycle, expert medians reach 5 across all INVEST criteria, and GPT‑5.1 converges strongly on Valuable and Testable while remaining more conservative on Independent and Small; semantic agreement exceeds 85–90% across most dimensions. These findings indicate that GPT‑5.1 could reduce manual assessment effort, reinforce structural quality, and support consistent requirements evaluation, while highlighting the complementary role of human oversight in industrial requirements‑engineering workflows.
Engineering Inferential Composition Control for Federated RAG in Data Spaces
Federated Data Spaces increasingly rely on Retrieval-Augmented Generation (RAG) to provide intelligent access to distributed and sovereign data sources. However, current RAG systems typically assume that retrieved fragments from different providers can be merged into a single generation context without semantic conflict. In regulated and heterogeneous settings, this assumption can trigger inferential errors, such as treating conditional or alternative requirements as jointly mandatory, posing risks to trustworthiness and governance. This paper reframes semantic interoperability in federated RAG systems as a problem of inferential composition rather than conceptual alignment. We introduce a lightweight engineering framework that adds an explicit reasoning control layer to the RAG pipeline. The framework uses semantic roles and pragmatic signals to guide selective context separation and conservative fragment composition, preventing unsafe inferences without requiring ontologies or shared vocabularies. We evaluate the approach through a reproducible case study in a regulated data-sharing scenario, showing that inferential composition control prevents reasoning failures under adversarial queries, while preserving benign integration. These findings highlight the need for explicit reasoning control in RAG-based systems operating in federated and regulated environments.
A Survey of Fuzzing Tools for JavaScript Engines
JavaScript is a critical programming language to Web applications. Code written in this language is processed and executed by specialized engines. Because these engines are complex and may contain vulnerabilities that enable attacks on end-user browsers or server-side applications, fuzzing has become the main technique to uncover security issues. Nevertheless, there is a lack of studies that characterize existing fuzzing tools for JavaScript engines and their main particularities. In this paper, we survey state-of-the-art fuzzing tools for JavaScript engines. For the 19 fuzzers identified, we analyze several dimensions related to the fuzzing steps, implementation details, and the benchmarks used. A variety of fuzzing techniques are employed, making the fuzzers more efficient and targeting specific classes of vulnerabilities. Popular JavaScript engines such as ChakraCore, JavaScriptCore, V8, and SpiderMonkey have been extensively tested using existing fuzzers. Finally, we discuss research insights to support researchers and practitioners in the development of new fuzzers for JavaScript engines.
HeatMetro: A 3D Code Visualization for Test Debt Analysis in Evolving Systems
Traditional test coverage metrics often fail to reveal test debt. Additionally, it is difficult to manually uncover code critical for testing, particularly in complex and rapidly changing components of large systems. However, few tools help developers visually explore test debt. In this work, we introduce HeatMetro, a web-based implementation of the code city metaphor that creates an interactive 3D software visualization and operationalizes a context-aware code coverage criterion for the visual analysis of test debt in large Java and Go projects. The feasibility of HeatMetro is evaluated through a study with developers and projects, which indicates promising results, confirming the tool’s effectiveness in identifying and explaining critical components for testing.
GEM: A Framework for Strengthening LLM-Generated Unit Tests Using Mutation Feedback
Large Language Models (LLMs) are increasingly used for automated unit test generation and can produce executable tests with substantial structural coverage. However, recent empirical studies indicate that such tests often rely on weak or superficial assertions, leading to limited fault-detection capability despite extensive code coverage. This paper introduces GEM (Generate-Execute-Mutate), an automated framework that systematically strengthens test oracles to improve the mutation-based adequacy of LLM-generated unit tests. GEM integrates three stages into a unified pipeline: LLM-based test synthesis, execution-driven self-repair of failing tests, and mutation-guided oracle refinement. The framework follows a modular hexagonal architecture and supports multiple programming languages through pluggable adapters for test execution, coverage analysis, and mutation testing. GEM was evaluated on three established benchmarks across Python, Java, and C++, using multiple state-of-the-art LLMs, and was compared with the automated testing tool Pynguin. Experimental results reveal a persistent gap between coverage and mutation score in baseline LLM-generated tests. Under the evaluated setup, mutation-guided strengthening improved mutation scores on Python and yielded smaller gains on Java, while execution-driven self-repair improved executability across several model and dataset settings.
Integrating Machine Learning and Model-Driven Engineering for User Interfaces: A Systematic Literature Review
User Interfaces (UI) operate in heterogeneous contexts and require adaptation and, in some cases, intelligent behavior. Machine Learning (ML) and Model-Driven Engineering (MDE) offer complementary capabilities to address this challenge; however, the evidence on their combined use in UI development remains fragmented. The literature still lacks an integrated synthesis of how ML and MDE are combined in this domain. This study aims to synthesize this evidence through a Systematic Literature Review conducted according to the guidelines of Kitchenham, Budgen, and Klotins. The findings cover UI typologies, ML roles and model families, MDE artefacts and paradigms, ML–MDE integration workflows, and coupling, and identify research gaps.
Towards Requirements Specification for Machine Learning-Based Software Systems
Machine Learning-based software systems (ML-based systems) have been increasingly developed for various domains, such as medicine, smart cities, and the automotive sector. These systems have posed significant challenges for Requirements Engineering (RE), including for requirements specification, while ensuring critical qualities such as reliability. The main problem addressed in this work is the difficulty practitioners have faced in specifying the requirements for these systems. Additionally, effective approaches (e.g., methods, techniques, processes, or guidelines) for this still lack consensus. The main objective of this work is to present and discuss current trends in approaches to requirements specification for ML-based s ystems. We observe this research field is very new, with weak collaboration with industrial environments. Novel approaches or adaptations of existing ones were proposed, but several possibilities for future research still exist; hence, we also discuss some of the main ones.
Promoting Successful Projects in a Software Engineering Capstone Course
Software capstone projects are successful when students achieve meaningful learning outcomes and clients obtain valuable results. For more than a decade, we have delivered a capstone course involving diverse external clients and domains, producing mostly successful projects; however, some initiatives have still fallen short. This study seeks to identify factors that foster or hinder project success in order to support instructors in structuring course dynamics and selecting appropriate projects and clients. We analyzed 18 software projects from the Spring 2024 term. At course completion, surveys were administered to both clients and student teams to evaluate technical competence, domain knowledge, time commitment, and collaborative attitude of each other. Five months later, follow-up semi-structured interviews with clients explored product value and retrospective perceptions. Survey and interview data were then cross-analyzed. Most projects were rated as successful across survey dimensions, with both groups highlighting commitment as a central factor. In less successful cases, students pointed to low client involvement or limited domain knowledge as key obstacles. At interview time, nearly all products were in use, although most required additional refinement. Even clients from less favorably rated projects generally expressed willingness to participate again, emphasizing students’ technical quality and hiring potential. Maximizing capstone success requires selecting clients who propose well-scoped, engaging projects and who understand the need for adaptive collaboration. Providing students with access to domain experts and end-users further strengthens alignment with business needs, improving both learning outcomes and practical value for clients.
Engineering a LLM-Based Data Ingestion Process for Semi-Structured Documents: An Empirical Comparison with Human-Centered Workflows
Data-driven software systems increasingly rely on ingesting information from semi-structured documents, whose manual processing is costly and error-prone. Although Large Language Models (LLMs) can interpret such documents, their systematic integration into reliable ingestion processes remains underexplored. This paper proposes an LLM-driven data ingestion process from a Software Engineering perspective, distinguishing prompt-based optimization from model training. Prompts are treated as versioned engineering artifacts and refined iteratively through automated comparison with ground truth under stateless execution. A controlled experiment evaluates multiple LLM backends using extraction quality, cost, and generalization.
Towards Hybrid and Reusable Type Inference for Dynamically-Typed Languages
Type inference is a technique for automatically reconstructing types before program execution in the absence of explicit type annotations. While it has been adopted in many statically typed languages, applying type inference to dynamically-typed languages, poses challenges due to their runtime-oriented typing mechanisms. In this paper, we present HYPHE, a Hybrid Type Inference and Validation Framework for dynamically-typed languages, designed to combine within a single environment various approaches to type inference to leverage the strengths of each. HYPHE infers, stores, compares, and refines type information in a continuous loop integrated with developers. A preliminary evaluation in the Pharo Smalltalk environment indicates that combining multiple inference sources, as established in prior works, achieves broad type coverage while shifting computationally intensive analyses to offline processing and reusing accumulated inference data.
A Cross-Level Traceable Modeling for Hybrid Quantum–Classical Software
Hybrid quantum–classical software requires engineering approaches that connect stakeholder goals, variability decisions, and architecture under NISQ constraints. We present Quantum Variability Management (QVM), a lightweight, requirements-driven modeling approach that integrates iStar, UVL, and Quantum-UML, through explicit cross-level traceability. QVM defines five rules that relate goals to features, constraints, component stereotypes, and configuration properties such as the provider and qubits. QVM focuses on the requirement-to-variability-to-architecture chain and on the auditable propagation of design changes. We assess the proposal through a scenario-based walkthrough in a logistics case, showing how changes in strategic intent, backend selection, and hardware quality assumptions can be propagated consistently across the three levels. The proposal provides a foundation for controlled reconfiguration in HQC systems, while broader user studies, multi-domain replications, and full tool integration remain future work.
Privacy Preservation in Textual Data: A Systematic Mapping Study on Differential Privacy and Semantic Similarity
Background: AI and machine learning increasingly depend on large-scale textual data, which often embeds personally identifiable and sensitive information. In this context, privacy-preserving processing of unstructured text has become essential to mitigate disclosure and re-identification risks, especially in pipelines that rely on semantic representations and similarity measures. Goal: This study aims to map state-of-the-art techniques for privacy-preserving textual data analysis and to characterize how privacy mechanisms relate to semantic similarity and the identification of infrequent (rare) textual patterns. Method: We conducted a Systematic Mapping Study (SMS) following established guidelines, retrieving peer-reviewed publications from four major digital libraries (2010–2025). The selected studies were screened using inclusion/exclusion criteria and a quality checklist, and the extracted data were synthesized through frequency-based and thematic mapping aligned with three research questions: (i) privacy-preserving techniques used in textual data analysis, (ii) computational approaches (data science and language-model-based methods) supporting such mechanisms, and (iii) techniques adopted for semantic similarity and rare-event oriented text analysis under privacy constraints. Results: The mapping shows that anonymization/de-identification, differential privacy, and federated learning are among the most recurrent privacy-preserving approaches reported for text. It also highlights the prevalence of NLP pipelines and transformer-based models (e.g., BERT variants and large language models) as supporting components, typically combined with classic semantic similarity techniques such as vector-space representations, embeddings, topic modeling, and cosine similarity. Rare-event detection appears less frequently, suggesting an emerging gap and opportunities for future research on privacy-aware pipelines for low-frequency phenomena in text. The results provide an evidence-based overview to support the design of guidelines and best practices for privacy-preserving text analytics in software intensive settings.
Designing Auditable and Version-Aware Consent Management Systems for Regulatory Compliance
Context: Data protection regulations such as the GDPR and the Brazilian LGPD impose strict requirements on consent management, including demonstrability, revocation support, accountability, and traceability. In practice, however, many existing consent management solutions fail to preserve a reliable historical linkage between user consent decisions and the specific versions of privacy policies in force at the time of collection, which undermines auditability and regulatory compliance. Goal: This paper aims to design a modular, scalable, and interoperable software architecture that supports the registration, explicit versioning, and auditing of user consent, ensuring traceability, integrity, and compliance with privacy regulations while preserving the historical binding between consent records and evolving privacy policies. Method: We follow a Design Science Research approach to derive architectural requirements from GDPR, LGPD, and relevant ISO/IEC standards, and to design a microservice-based consent management architecture. The proposed solution is instantiated as a proof of concept composed of independent services for policy management and consent recording, exposed through RESTful APIs. The architecture is demonstrated in a controlled environment through end-to-end consent lifecycle scenarios, including policy versioning, consent granting and refusal, revocation, and audit querying. Results: The proof of concept shows that explicit policy versioning, cryptographic integrity mechanisms, and event-based consent recording can be combined to preserve immutable historical records of consent decisions. Each consent, refusal, and revocation event is deterministically bound to a specific policy version, enabling consistent audit queries by user, policy, and time. Conclusion: The study indicates that a version-aware, microservice-oriented design provides a feasible foundation for auditable and regulation-compliant consent management. By treating privacy policies as versioned artifacts and consent as a persistent event history, the proposed architecture bridges regulatory requirements and implementable software mechanisms.
Distinguishing Software Aging from Stability in Open Source Projects: An Empirical Analysis of Activity Indicators
Open-source software projects may experience variations in development activity over time. However, a decline in activity does not necessarily indicate software aging, as mature projects may require fewer changes over time. This study aims to distinguish software aging from stable maturity by analyzing activity indicators empirically. We conducted a longitudinal analysis of three open source projects, examining monthly trends in commits, active contributors, issues, and releases. The results show that stable projects exhibit low but consistent activity, whereas aging projects are characterized by abrupt and sustained declines across multiple indicators. The joint analysis of longitudinal activity indicators enables distinguishing between stability and software aging.
A Container-Based Software Architecture for Coupling Heterogeneous Agent-Based Evacuation Models using Machine Learning
Large-scale disaster simulations often require the integration of heterogeneous systems, such as indoor egress models and city-wide traffic simulators. However, coupling these systems presents significant interoperability and scalability challenges due to high computational costs. This work proposes a modular software architecture to bridge this gap. We developed ”Oráculo”, a containerized microservice system that uses Machine Learning (ML) surrogates to encapsulate the complexity of microscopic Agent-Based Simulations (ABS). The architecture, orchestrated via Docker, allows outdoor simulators to query indoor evacuation rates through a lightweight API, decoupling the internal logic from the external flow. Validation results demonstrate that the ML component achieves an R2 of 0.87, proving that high-fidelity simulation dynamics can be effectively served as efficient software components.
Security Practices in Agile Development: An Industry Survey on Adoption, Perceived Impact, and Measurement in DevOps
This paper presents an empirical survey study (N=24) examining the integration of security practices into agile and continuous development processes. The findings reveal that while the most adopted practices include Standards and Requirements (71%), Code Review (67%), and Compliance and Policy (63%), their implementation remains predominantly non-systematic. Despite moderate to high perceived impact, significant gaps exist between recognition and effective adoption, compounded by low security expert participation (37%) and a notable scarcity of metrics. The findings provide exploratory evidence on the current state of practice and key priorities for organizations looking to strengthen security integration while maintaining agility in their development processes.
Classifier Ensembles for Software Development Team Effort Estimation: A Rapid Systematic Literature Review
This work presents a systematic literature review (SLR) on the use of classifier ensembles for software team effort estimation. The study analyzes the advantages of homogeneous and heterogeneous ensembles, highlighting how the combination of algorithms improves prediction accuracy. A total of 27 relevant studies were examined, using metrics such as MAE, RMSE, and PRED to assess model performance. The results indicate that heterogeneous ensembles are more effective in environments with high variability, while homogeneous ensembles excel in specific domains. The work suggests the exploration of hybrid metrics and dynamic optimization for future research.
Modelos de Inteligencia Artificial Aplicados a la Gestión de la Mora en las Administraciones Tributarias Subnacionales
La gestión de la morosidad tributaria constituye uno de los principales desafíos para las administraciones tributarias subnacionales, debido a su impacto directo en la sostenibilidad financiera, la equidad del sistema tributario y la eficiencia del gasto público. Los enfoques tradicionales de recaudación suelen ser reactivos, basados en reglas estáticas y con alta dependencia de procesos manuales, lo que limita su efectividad frente a entornos económicos dinámicos. Esta investigación doctoral propone el diseño de un modelo basado en Big Data e Inteligencia Artificial orientado a mejorar la eficiencia en la gestión de la morosidad fiscal. La propuesta integra algoritmos predictivos para anticipar el incumplimiento tributario y modelos de segmentación automatizada que permiten clasificar a los contribuyentes según su perfil de riesgo fiscal, incorporando además mecanismos de automatización para personalizar las estrategias de recaudación y seguimiento. El estudio se desarrolla bajo un enfoque de Design Science Research y se fundamenta en el análisis de datos históricos de recaudación, comportamiento de pago y variables contextuales relevantes. Los resultados preliminares indican que los modelos predictivos permiten identificar patrones de morosidad con mayor precisión que los enfoques tradicionales, favoreciendo una gestión proactiva basada en el riesgo. Actualmente, el trabajo se encuentra en una etapa intermedia de desarrollo y se espera que sus aportes resulten relevantes tanto para la investigación académica como para la práctica administrativa en el sector público.
Toward a Systematic Evaluation of Usability in RESTful APIs: A Metric-Based Approach
In recent years, web APIs have become a crucial part of modern software development, as they allow easy access to a wide variety of resources and services. This has led to a competitive market, “the API economy”, and as a consequence, usability now plays a key role in the success of web APIs. Despite that, usability is often overlooked because there is no standard way to measure the usability of a web API. This work aims to address this problem by proposing a comprehensive model that enables systematic assessment of web API usability from various perspectives.
Ensuring Data Sovereignty in IoT Systems for Cognitively Impaired Seniors through Delegated Data Management
IoT technologies for seniors improve independence and well-being, but managing sensitive health data can be difficult for those with cognitive impairments. The existing PDS architecture lacks a formal delegation mechanism to enable third parties to manage data on behalf of users. Using the DSR approach, the study proposes a PDS-based delegated data management framework with a delegation-aware component that mediates access requests, enforces scoped and time-bounded authority, and records auditable decision trails. The expected contribution is a capacity-aware, sovereignty-aligned mechanism for secure delegation that preserves residual autonomy while reducing risks of misuse and exploitation.
Hacia una Arquitectura Híbrida Extensible para Sistemas IA-Ready: Diseño y Resultados Preliminares
La incorporación creciente de inteligencia artificial (IA) en sistemas de software ha puesto en evidencia limitaciones estructurales en las arquitecturas tradicionales, particularmente en términos de acoplamiento y capacidad de evolución. A pesar del avance en modelos y técnicas de IA, la literatura reciente muestra una ausencia de arquitecturas genéricas diseñadas para integrar estas capacidades como componentes de primer nivel. Este trabajo presenta una investigación doctoral orientada al diseño de una arquitectura híbrida extensible para sistemas IA-ready, cuyo objetivo es desacoplar la lógica de negocio de los componentes de inteligencia artificial y facilitar su integración en entornos heterogéneos. Como resultado preliminar, se desarrolló un scaffolding funcional que implementa los principales componentes de la arquitectura propuesta, permitiendo validar de manera inicial su viabilidad conceptual y técnica.
Hybrid Model for Improving the Accuracy of Software Development Team Effort Estimation with Genetic Algorithm-Optimized Regression Ensembles
This thesis plan aims to propose a Hybrid Model for Software Development Team Effort estimation by integrating Regression Ensembles and GA. The methodology will consist of an experimental evaluation of the proposed machine learning model using multiple datasets from the PROMISE repository, employing the evaluation metrics MAE, RMSE, MdMRE, PRED(25), and R2 score to analyze predictive performance.
Agentes basados en LLMs para la derivación de tareas de desarrollo backend a partir de requerimientos textuales
El desarrollo backend presenta desafíos para transformar requisitos funcionales informales en tareas precisas e implementables. Este trabajo explora el uso de Modelos de Lenguaje de Gran Escala (LLMs) como agentes colaborativos y orientados a tareas para apoyar las etapas tempranas del ciclo de vida del software. Se propone un flujo de trabajo modular multi-agente en el que un conjunto de agentes especializados asisten a un desarrollador a refinar requisitos, descomponer funcionalidades en servicios, definir contratos de API, especificar suites de pruebas y asegurar consistencia. El objetivo es reducir el esfuerzo manual, la sobrecarga de coordinación y posibles desalineamientos, mejorando la velocidad y calidad del desarrollo.
Operationalizing Software Antipatterns for Automated Detection Using Structural Metrics and Supervised Learning
This doctoral research aims to develop and validate a machine-learning approach for detecting software antipatterns during the construction phase. The study addresses three central challenges: ambiguous taxonomies and definitions of antipatterns, elevated false-positive rates in detection, and limited reproducibility of learning-based results without standardized protocols. The proposed pipeline consolidates taxonomy and formal definitions, operationalizes antipatterns as logical compositions of code smells and measurable structural properties, and derives observable indicators from structural metrics. Supervised learning with explicit hyperparameter optimization and a metric–label correlation layer are planned to support robustness, interpretability, and traceable evidence.
ArchiGenMS: A Neuro-Symbolic Evolutionary Framework for Greenfield Microservice Design
Projetar microsserviços a partir de requisitos greenfield continua sendo desafiador porque artefatos textuais iniciais são ambíguos e a geração com LLMs pode produzir decomposições estruturalmente inconsistentes. Este artigo apresenta o ArchiGenMS, um framework neuro-simbólico que combina exploração semântica guiada por LLMs com um núcleo computável de validação em Lean 4 dentro de um laço evolucionário elitista. O framework opera sobre uma abstração arquitetural inicial de serviços, operações, parâmetros e chamadas entre serviços. Em 22 conjuntos de dados do corpus de Dalpiaz, a porcentagem de candidatos válidos em Lean cresce de 74,5% para 99,5% em cinco gerações, enquanto a proxy média de coesão melhora de 0,29 para 0,23. Esses resultados posicionam o ArchiGenMS como uma abordagem reproduzível e formalmente restrita para descoberta de microsserviços assistida por IA.
Automated Fuzz Driver Generation for Java via Search-Based Testing: A Preliminary Study
Coverage-guided fuzzing has proven to be remarkably effective in uncovering security and reliability issues across large software systems. However, its adoption in practice remains hindered by the need for manually crafted fuzz drivers, whose construction is labor-intensive, error-prone, and often unrealistic for open-source software, where thousands of libraries expose complex and interdependent APIs. In this paper, we present arrabida, a novel approach to automating the generation of fuzz drivers for Java through encapsulated controlled execution contexts and orchestrated use of APIs, such as unit test cases. arrabida integrates search-based test generation (via EvoSuite) with static analysis, transforming the synthesized tests into Jazzer-compatible fuzz drivers, which are compilable and deployable in OSS-Fuzz. We evaluated arrabida on three real-world Java projects from OSS-Fuzz, by comparing generated drivers with their manually written counterparts in terms of iteration throughput, coverage, feature exploration, and crash detection. Our results show that arrabida drivers achieve comparable results to manually written drivers, exceeding coverage and number of iterations, while discovering unique program features and behaviors. arrabida triggered 11 unique crashes.
DELFOS (Decision Support System based on Machine Learning Algorithms Designed to Detect Hidden Genetic Pathologies by Big Data Analysis of Electronic Medical Records) es un sistema experto de soporte a la decisión clínica (DCSS) basado en Big Data e inteligencia artificial (IA) orientado a mejorar el asesoramiento genético mediante la integración de historias clínicas electronicas (HCE) y datos genómicos. El proyecto, financiado por Red.es en la convocatoria 2021 de proyectos de I+D en IA (C005/21-ED) y desarrollado por G7 Innovations Solutions SL en colaboración con la Universidad de Sevilla e INEBIR1, busca impulsar la transformación digital en clínicas de reproducción asistida, donde la demanda de asesoramiento genético supera la disponibilidad de genetistas clínicos y se prevé que aumente con el avance de la medicina personalizada. DELFOS se integra con historias clínicas electrónicas mediante el estándar FHIR (Fast Healthcare Interoperability Resources) y, en su estado actual, ha sido validado en un piloto clínico en INEBIR para dos modelos predictivos (infertilidad masculina y endometriosis). En infertilidad masculina, el piloto con 40 pacientes obtuvo un 47,5% de acierto, un 7,5% de predicciones parcialmente correctas y un 45% no coincidentes con el diagnóstico clínico, destacando la necesidad de ajustar umbrales, especialmente en teratozoospermia. En endometriosis, el piloto con 14 pacientes mostró un 100% de acierto cuando existían datos genéticos disponibles y una reducción significativa del rendimiento cuando no se disponía de dicha información.
GRAICE-DELFOS: Towards a GenAI-Powered Framework for Continuous Cybersecurity and Safety Compliance in High-Risk Healthcare AI Systems
Ensuring AI safety and cybersecurity compliance for high-risk healthcare AI is complex under the EU AI Act, NIS2, and Cyber Resilience Act. This paper introduces GRAICE, a GenAI-based framework for automated remediation and continuous regulatory alignment, integrated into DELFOS, a clinical support tool for genetic diagnostics. By embedding GenAI agents into the AI lifecycle, the system replaces static audits with a continuous, evidence-driven compliance and cybersecurity continuum. Expected results include enhanced resilience, automated risk mitigation, and increased clinical trust.
Building an Inclusive and Sustainable Smart City: A Pilot Project on Decentralized Digital Identity and Data Governance in Santa Rosa
This paper presents the initiative to transform the municipality of Santa Rosa into an intelligent and sustainable region. The project is fundamentally guided by the principles of Equality, Diversity, and Inclusion. For its implementation, a smart city pilot lab was built to validate technologies in a real-world environment. The study focuses on exploring decentralized solutions that enable citizens to securely access urban data through digital identification. The experimental evaluation of access control utilizes the concept of Decentralized Digital Identity, supported by Hyperledger Indy and Trustchain technologies. This research aims to advance the debate on the integration of decentralized identity into smart city infrastructure.
Automated testing framework to evaluate multi-agent chat assistants
This applied R&D project proposes an automated framework for evaluating multi-agent conversational assistants equipped with retrieval-augmented generation (RAG) capabilities. The solution addresses the high cost and time demands of manual evaluation by introducing a synthetic persona dataset and an automated pipeline that executes large-scale tests on mobile devices. Tests with English and Portuguese personas revealed recurring weaknesses in multi-agent systems, particularly in Portuguese interactions, highlighting the importance of multilingual evaluation. The project is a collaboration between INDT and Motorola Mobility and aims to provide a systematic methodology and testing infrastructure for industry conversational systems.
A LoRaWAN-Based Infrastructure for Meteorological Data Collection to Support Software Systems in Agriculture
This paper describes the deployment of a LoRaWAN-based infrastructure in the municipality of Santa Rosa, RS, Brazil, integrating meteorological stations, gateways distributed across urban and rural areas, and a software stack composed of ChirpStack, Node-RED, and PostgreSQL for continuous environmental data acquisition. Unlike purely conceptual approaches, this work presents a real operational environment and its applications. The collected data include temperature, humidity, precipitation, wind, luminosity, and UV radiation, which are made available through dashboards for real-time monitoring. The main contributions are: (i) the implementation of a functional infrastructure, (ii) support for agricultural applications based on real-world data, and (iii) the identification of indicators and use cases to support decision-making, such as phytosanitary risk analysis. Finally, perspectives for system evolution, evaluation, and future research are discussed.
Using Technology to Support Healthcare Access in the Northern Regions of Brazil – A Telemedicine-based Approach
Background: Access to primary healthcare services remains uneven in Brazil, particularly in the North region, where geographic distance, low medical density, and logistical constraints limit access to basic healthcare for remote communities. Aims: This paper presents a research and development project that aims to support communication between healthcare professionals and residents of remote communities in the North of Brazil through the use of telemedicine resources. Method: We conducted field visits and technical assessments in North communities to identify infrastructural, connectivity, and logistical constraints. Based on this analysis, we define aspects such as healthcare workflows, usage scenarios, and a telemedicine solution architecture, including a portable booth and a supporting digital platform. Results: We specified modules for vital signs collection, real-time video-based consultations, automatic transcription of medical consultations, and summary generation to support medical anamnesis. It also includes mechanisms for data security and privacy, as well as support for speech recognition and translation in indigenous languages. Conclusion: The current state of the project establishes a technical baseline that enables the implementation and evaluation of remote examinations and telemedicine consultations in real-world settings.
MLEvol Project: Hacia la Evolución Continua y Eficiente de Sistemas de ML Un enfoque dirigido por el Ecosistema
El Aprendizaje Automático es un pilar clave de la innovación digital y ha dado lugar a un ecosistema dinámico y creciente de activos reutilizables, tales como modelos, datos, algoritmos, y herramientas. Sin embargo, la selección e integración de estos activos en sistemas software basados en aprendizaje automático sigue siendo un reto, especialmente cuando estos sistemas deben evolucionar de forma continua y cumplir requisitos de calidad, confiabilidad y regulación. El objetivo de este proyecto es proponer un marco basado en métodos, prácticas y herramientas de Ingeniería de Software para dar soporte a la evolución continua y eficiente de los sistemas software basados en aprendizaje automático, guiada por el ecosistema actual de activos existentes.
COMPAS: COllaborative process Mining with robotic Process automation and generative AI for the design and Sustainability of hyper-connected processes
Process Mining (PM) provides techniques for analyzing event logs to support evidence-based improvement and process discovery. However, multi-organizational collaborative processes remain difficult to design and analyze due to their distributed nature and technological diversity. Recent advances in Large Language Models and Robotic Process Automation offer new opportunities to support process discovery, modeling, and automation. In parallel, Green BPM emphasizes the need to incorporate sustainability considerations into process design and execution. This project aims to define and evaluate techniques, algorithms, methodologies, and strategies for integrating PM with RPA and Generative AI, considering sustainability elements for the design, automation, and analysis of hyper-connected collaborative processes for e-Government, leveraging real data from the Uruguayan Digital Government Agency (AGESIC), linking theoretical and practical results.
ATHENA – Tecnologías Avanzadas para Entornos y Aplicaciones Híbridas Clásico–Cuánticas
A pesar de los avances recientes, la adopción industrial de la computación cuántica sigue careciendo de enfoques adecuados de Ingeniería del Software Cuántico (QSE) que permitan integrar servicios cuánticos en sistemas complejos de forma sistemática, gobernable y segura. En este trabajo se presenta ATHENA, un proyecto de investigación aplicada que aborda este reto desde la perspectiva de los Hybrid Software as a Service (HSaaS). ATHENA propone un enfoque basado en QSE para el diseño, desarrollo y operación de HSaaS, combinando arquitecturas híbridas, calidad y pruebas, seguridad desde el diseño y gobernanza basada en contratos, con el objetivo de facilitar la transición hacia sistemas software híbridos de nivel industrial.
MATIIGEN: Model-Driven Software Engineering Meets Precision Medicine for Improving Woman Healthcare
Software engineering is a fundamental pillar for developing quality healthcare systems, with speciall atention in the Precision Medicine field, which tailors treatment based on individual genetic, environmental, and lifestyle variability. In this context, the MATIIGEN project aims to transition an intelligent software system for automated DNA variant interpretation from laboratory validation (TRL4) to operational clinical settings (TRL6). MATIIGEN is centered on Lipedema early detection, a chronic and widely under-researched disease primarily affecting women. Despite its high prevalence and significant physical and psychological impact, Lipedema was only recognized by the WHO in 2018, leaving its genetic basis largely unexplored. By applying a Model-Driven Software Engineering approach to the Lipedema use case, MATIIGEN will demonstrate how bridge the gap between management of complex clinical data and the descovery of actionable clinical insights, ultimately providing a systematic framework for developing high-quality systems in precision medicine.
MAPE-KV: Una Arquitectura Neuro-Simbólica Verificada para la Orquestación de Enjambres Robóticos
Robotic swarm orchestration in the Edge–Cloud Continuum increasingly depends on adaptive planning, yet the use of LLMs introduces stochastic risk into safety-critical cyber-physical systems. We present MAPE-KV, an extension of the MAPE-K loop that inserts a formal verification gate in Lean 4 between planning and execution. The proposal aims to block unsafe plans ante hoc through explicit physical-safety and software-quality invariants. A preliminary proof of concept in the Ester Grid simulator with five robots showed that, in the evaluated scenario, the gate intercepted all unsafe plans observed before execution. This work should be read as an architectural proposal with initial feasibility evidence, not as exhaustive experimental validation.
From Learning Management Systems to Adaptive Learning Systems: A Systematic Architectural Transformation Framework
Traditional Learning Management Systems (LMS) predominantly operate as static content repositories under a ”one-size-fits-all” paradigm, failing to address the diverse cognitive needs and learning paces of individual students. While Adaptive Learning Systems (ALS) offer a solution through personalization, migrating legacy monolithic LMS infrastructures involves overcoming significant technical debt and ”architectural stiffness.” This paper proposes ARALS (Adaptive Reference Architecture for Learning Systems), a framework designed to evolve legacy LMSs into genuine adaptive environments without requiring a complete codebase rewrite. To this end, we introduce a Conceptual Transformation Sequence (C1) and the PALS framework (Properties of Adaptive Learning Systems) as a systematic method to inject and validate adaptivity. ARALS integrates the MAPE-K autonomic computing cycle into a decoupled, seven/eight-layer structure, acting as a control mechanism for the operational flow to ensure pedagogical adaptation. The proposal is validated through a high-fidelity case study in which the Odoo eLearning module is architecturally refactored. Results demonstrate the successful implementation of dynamic ”Learning Roadmaps” and personalized content sequencing with negligible performance overhead, proving the viability of ARALS as an interdisciplinary bridge between software engineering and adaptive pedagogy.
Reducing Rework in Software Projects through Size and Complexity Metrics
This paper proposes a model for decomposing software projects into smaller units based on the Size and Complexity Points (PTC) metric, which accounts for effort in person-hours and the required level of business rule knowledge. The primary objective is to reduce rework and enhance development efficiency. The model’s evaluation was conducted through a case study in a software development company within the transportation sector, comparing a traditional management model with the proposed approach. The results indicate a significant reduction in rework, alongside improvements in project planning, delivery quality, and overall process efficiency.
SoftGirls: An Institutional Strategy at Pascual Bravo to Promote Female Inclusion in Software Engineering Education
SoftGirls is an initiative developed within the Faculty of Engineering at the Pascual Bravo University Institution, aimed at strengthening both technical and soft skills among female students from the academic community. The objective of this article is to present the work carried out by the SoftGirls study group, which focuses on enhancing its members’ competencies in computer science, specifically in programming, UX/UI design, and emerging trends in software engineering. The study group has been active since mid 2024 and integrates in-person sessions, visits to software industry companies, talks and training courses delivered by expert professionals, as well as an institutional certification of the acquired competencies issued by the educational institution.
Exploring the Measurement of Productivity in Agile Teams in the University Context: Acceptance and Comprehensive Qualitative Assessment
Productivity measurement in agile software development (ASD) teams is an important topic in industry because it provides essential information for decision-making and helps adjust team behavior to support continuous improvement. However, developing the necessary competencies in undergraduate students to incorporate this process through a Software Engineering course has become a challenge due to the high level of abstraction involved. This article presents the main findings from the adoption of a productivity measurement process implemented with 110 students from Higher Education Institutions in Colombia. Organized into agile teams, the students developed a software product. The results show a high level of acceptance of the proposed measurement process. The main difficulties encountered were related to the practical implementation of metrics. Regarding areas for improvement, the use of a computational tool to support the proposed process is suggested in order to reduce its level of complexity.
Requirements Engineering for Data Products: Adapting User Stories
This paper proposes recommendations for adapting user stories as a tool for capturing and managing requirements that support data-based functionalities (dashboards, AI models, among others). To conclude, aspects such as collaboration between roles, verifiable acceptance criteria, integration with model metrics, and support for data science tools and algorithms are considered.
QuantumX: una experiencia para la consolidación de la Ingeniería de Software Cuántica como disciplina emergente
Robotic process automation (RPA) and agents with BPMS to increase autonomous capability and operational improvement in organizations: a practical assessment
Robotic Process Automation (RPA) tools have recently incorporated autonomous LLM-based agents to automate complex human-interaction tasks without disrupting internal systems. Its use within Business Process Management Systems (BPMS) enables process optimization, error minimization, and reduced time commitment. This study presents a practical evaluation of UiPath, Microsoft Power Automate, and Automation Anywhere, and their integration with Camunda BPMS through three automation scenarios. The results indicate that all tools contribute to increasing autonomous capacity and improving operational processes, with strengths in different characteristics.
On the Need to Compare the Architecture Design of Large Language Models: A Preliminary Reference Metamodel
A large number of Large Language Models (LLMs) have been developed and made available to the community. However, analyzing and comparing these models remains challenging since there is no clear, standardized way to describe their architectures precisely. While LLM developers typically provide textual model cards, configuration files, or source code, these artifacts do not follow a common standard. Consequently, researchers need to make additional efforts to reuse and compare existing LLMs. To address this issue, we propose a conceptual modeling approach to create a model-driven method for describing and analyzing LLM architectures.
DMAP: An Artifact for Identifying Scrum Antipatterns in Software Projects
Identifying software project management antipatterns in practice remains difficult because their evidence is often dispersed and hard to interpret. This paper presents DMAP, an artifact for detecting Scrum antipatterns and mapping them to management antipatterns. DMAP integrates a case-selection protocol, a dichotomous questionnaire, and an NPLF mapping scale. Evaluated on 164 case studies, the artifact selected 5 for detailed analysis and identified 7 Scrum antipatterns (12 occurrences) mapped to 5 management antipatterns. The results suggest that Scrum deviations can serve as indicators of underlying management problems.