Proceedings of the Ibero-American Conference on Software Engineering (CIbSE)
This document presents the Foreword and the Organizing Committee of 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.
Integration of a tool for agile monitoring through the use of Machine Learning
The agile approach has driven the need for advanced tools in project management, especially in multi-project environments. This work presents the integration of a Machine Learning (ML) model into Worki, an agile tracking platform, to analyze historical Sprint data and evaluate performance. The model classifies and assesses progress and work pace, displaying the results directly within the platform. This enables a data-driven perspective, facilitating early anomaly detection and improving project management efficiency.
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.
Evaluating the quality of user stories using large language models: an industry study
The specification and maintenance of high-quality user stories are critical and challenging activities in agile software development, due to the dynamic nature of projects, the ambiguity of natural language, and the effort required for manual evaluations. This study investigates the use of large language models (LLMs) to assess the quality of user stories in an industrial software project, using the criteria defined by the INVEST framework. The performance of three LLM tools is compared with two evaluations conducted by requirements engineering experts. The results indicate that LLMs have the potential to support the automated assessment of user stories based on INVEST.
Extended Feature Model for Hybrid (Quantum-Classical) Systems
Quantum computing (QC) and quantum software engineering (QSE) face challenges due to the diversity of algorithms, languages, and integration strategies. This paper proposes a quantum-classical hybrid software development model following a Software Product Lines approach. Based on a previous model and a literature search, essential components were identified and structured in a hierarchy to represent the variability in these systems. The resulting model facilitates variability and configuration management of hybrid architectures, setting the basis for further evaluation and refinement and contributing to a more effective methodology for integrating QC and QSE.
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.
Unit Test Generation with LLMs in Industrial Environments: Challenges, Evolution, and Practical Lessons
Large Language Models (LLMs) show potential for automatically generating unit tests, but their industrial application presents challenges. We present a longitudinal case study on the implementation and evaluation of LLMs for test generation at LKS Next, integrating standard tools such as SonarQube. Our approach reveals findings on the temporal evolution of these technologies in production environments and provides learned lessons. The results offer evidence-based industrial guidance for organizations considering adopting these solutions, highlighting practical integration and maintainability considerations often absent in theoretical studies.
Trip to the Moon - RPG applied to teaching Software Testing with elements of Socially Aware Design
This paper presents the use of a framework for creating RPGs (Role-playing Games) applied to Software Engineering (SE) teaching to stimulate active participation and increase students’ protagonism in the teaching and learning process. The framework, named CreativEduc, has basic elements for creating RPGs combined with Socially Aware Design (SAwD) artifacts, which allows obtaining a systemic and social view of the problem. An RPG was created for the Software Testing discipline of an undergraduate SE course. The planning, execution and evaluation of the game and the framework are reported. Questionnaires were applied, resulting in positive indications regarding its use and application.
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.
Personal Factors Influencing Task Performance under Test-Driven Development: Meta-Analysis of a Family of Experiments
Background: Experimentation in Software Engineering often requires experimental subjects to perform some task. Objective: To study the extent to which experimental tasks are carried out based on an experiment’s instrumental aspects and the subjects’ personal characteristics. Method: Metaanalysis of a family of experiments in Test-Driven Development. Results: Older subjects and those with more professional experience perform less work on the experimental tasks. The nature of the experimental task can influence the level of participation in the experiment. Conclusions: The motivations of experimental subjects should be studied, and control mechanisms should be established to ensure proper performance.
Cybersecurity reference model for preventing network attacks on critical infrastructure in the quantum era
This paper presents a cybersecurity reference model for protecting cyber-physical systems (CPS) in critical infrastructures (CI) from quantum computing threats. The systematic literature review (SLR) reveals the urgency of incorporating post-quantum cryptography and addressing challenges such as interoperability with legacy systems and high implementation costs. MoRCiTO integrates access controls, continuous monitoring, and network cyberattack detection and mitigation methods, developed through action research in three cycles.
Towards a reference architecture for hybrid software, adapted to the emerging needs of Quantum Software Engineering
The rise of quantum computing has introduced a new paradigm in information processing, with the potential to surpass the limits of classical computation. However, the development of software architectures capable of fully leveraging this technology remains an active research area, filled with unprecedented challenges. This paper presents the current state of a Ph.D. proposal focused on designing a reference architecture to support the definition of hybrid software systems, addressing the needs and challenges emerging from the progress in Quantum Software Engineering.
Pulmonary Pathology Detection: An AI-Based Approach with Convolutional Neural Networks
Early diagnosis of lung diseases significantly improves survival. This study uses an action research approach with CNNs, ResNet152, and Vision Transformers, optimized with data augmentation. Applying the ISO 25059:2023 standard, the study addresses data interpretability, robustness, and security. Initial results show an accuracy of over 93%, supported by heat maps that highlight key areas in the images, facilitating clinical work. Ethical principles are integrated to protect patient privacy. The project seeks to reduce diagnostic errors and promote the safe use of artificial intelligence in hospitals, with a view to future clinical validation.
Agile Microservices Identification Using DDD and BDD
Adopting microservices improves maintainability and scalability compared to monolithic architectures, though initial design remains challenging. This research proposes an agile framework combining Behavior-Driven Development and Domain-Driven Design to identify microservices and define scalable architectures. A literature review and an experiment show that integrating strategic design with agile practices reduces technical debt and enhances scalability.
Exploring the Use of Artificial Intelligence in Microservices Discovery and Design
Microservice design is a significant challenge in greenfield software development, especially in ensuring internal cohesion and low coupling. This paper proposes an artificial intelligence-based approach, particularly leveraging natural language processing (NLP) and deep learning, to support the discovery and design of microservices from textual requirements. Employing the Design Science Research (DSR) methodology, the proposed research focuses on prompt engineering techniques and architectural validation to guide and assess the quality of microservice-oriented designs. As part of a doctoral project, this work aims to optimize architectural quality, design time, and costs, addressing challenges and opportunities identified through both a systematic mapping study (MSL) and a systematic literature review (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.
Towards the development of an adaptive model for managing social debt in distributed agile software development environments
The misalignment of socio-technical congruence in software development teams—understood as the disconnect between social and technical factors—can lead to suboptimal decisions and unforeseen costs, giving rise to the concept of social debt. Through a systematic literature review, various solutions for detecting social debt have been identified; however, these approaches operate in isolation. Therefore, this study proposes the development of an adaptive model to manage social debt in distributed agile teams, integrating its causes, effects, and mitigation strategies. The model is grounded in an Action Research approach structured into four iterative cycles.
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.
Talent retention strategies in digital transformation and their impact on technology staff turnover
The turnover of personnel in the Information Technology (IT) sector is a key challenge in digital transformation. This doctoral work investigates the relationship between retention strategies and turnover reasons in IT companies in Brazil. The hypothesis suggests a significant relationship between these strategies and the professionals’ decision to stay or leave. The methodology combines qualitative interviews and thematic analysis, along with a multi-agent system (MAS) simulation in GAMA to assess key factors in turnover. Preliminary findings show that, despite applied strategies, turnover remains a challenge, especially given high competitiveness and technological evolution. This study contributes to the development of more effective human resource management policies.
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.
Promoting critical thinking among IT project managers in the GenAI adoption process
This research focuses on IT project managers, analyzing their critical thinking skills and behavior in complex decision-making. The main objective is to provide tools to strengthen this competency, which could become a key differentiating value for these professionals in the process of Generative AI (GenAI) adoption.
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.
Development and Evaluation of an Intelligent Tutor for Programming Learning Based on Extensive Language Models
The emergent behavior of automatic programming following the popularization of Generative Artificial Intelligence has raised uncertainty about the future of programming and its teaching. This doctoral work proposes the design and evaluation of an architecture for the development of Intelligent Tutoring Systems for programming learning, integrating Large Language Models to provide a personalized user experience. The architecture is developed using a design-based research methodology, assessing its effect on cognitive engagement and learning through formative prototype evaluations and a summative evaluation conducted via an intervention study.
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).
Explainability Requirements Engineering in Machine Learning Based Systems
Explainability is the ability to make the predictions of a Machine Learning (ML) model clear and understandable to people. The adoption of ML models in critical decision-making requires explainability to be incorporated as a fundamental requirement in the decision-making process. As a recent topic, explainability in ML-based systems still lacks systematized processes, which opens space for further research. This PhD work aims to develop and contribute with a Requirements Engineering approach for the analysis and specification of explainability requirements in ML-based systems. The Design Science methodology will serve as the model to guide this research.
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.
Process mining and artificial intelligence for the design, sustainability, and improvement of collaborative processes
In recent decades, process mining has played a key role in improving organizational processes. Today’s growing complexity, with collaborative processes involving multiple actors, demands new perspectives for design and improvement. At the same time, sustainability and artificial intelligence (AI) have gained relevance. This doctoral work aims to expand the scope of process mining and AI by integrating techniques, practices, and tools for the design, sustainibility and improvement of collaborative processes.
Platform for Spatial Data Management and its Application in Health Management
Different organisms generate spatial data. Health organizations have their own data that could be spacialized. Using these data together allows for spatial analysis to support health service management and planning. However, joint analysis of such data presents several challenges. This paper proposes mechanisms that facilitate joint data management to support access assessment and health service planning. Specifically, it proposes a platform, built using the Design Science Research approach, to generate the design and implementation iteratively. It will be evaluated through exchanges with health experts and technicians.
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.
Architecture of a serious VR video game to obtain personalized educational strategies for the development of specific cognitive skills
Mathematical learning is crucial, and cognitive skills are key to enhancing it. In Mexico, deficiencies in mathematics from early education pose an educational challenge. This article presents a scalable and modular architecture for a serious virtual reality game, based on MVC, designed to foster mathematical learning and personalized cognitive development. Applying a hybrid methodology (Design Thinking, ADDIE, and the Game Development Process), the proposal is an educational solution that leverages personalization algorithms, along with the user's lifestyle and cognitive performance, to tailor a strategy plan that enhances their cognitive skills and mathematical learning.
European Innovation Alliance for Teaching Education ENACTEST Project
The importance of software testing is undeniable, but its lack of proper implementation can lead to applications with errors and problems. This situation is due to a mismatch between the skills demanded by industry, the learning needs of students, and the current methods used to teach testing in educational institutions. The ENACTEST project seeks to address this gap by developing software testing training packages tailored to industry demands and students' cognitive processes. The consortium includes universities, vocational training centers, and small companies, representing a broad socioeconomic environment.
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
This paper does not include an abstract.
An API for Developing Secure Integration Processes
How do software engineers perceive their productivity when working remotely?
Remote work became more established after the COVID-19 pandemic. As large companies look to return to the office, this study analyzes the perceptions of software engineers in Mexico, Spain, Colombia, and Argentina regarding teleworking.
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.
Microservices Access Authorization Management in Public Management Systems: An Automation Proposal
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.