arXiv subjects

Ronnie de Souza Santos

Publications and source records attributed to Ronnie de Souza Santos.

At least 73 records · Page 4Linked to original sources

arXiv2025

Not real or too soft? On the challenges of publishing interdisciplinary software engineering research

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

cs.SE↗

arXiv2025

Software Fairness Debt

As software systems continue to play a significant role in modern society, ensuring their fairness has become a critical concern in software engineering. Motivated by this scenario, this paper focused on exploring the multifaceted nature of bias in software systems, aiming to provide a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiency in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt, which complements the notions of technical and social debt, encapsulating the accumulation of biases in software engineering practices while emphasizing the societal ramifications of bias embedded within software systems. Our study contributes to a deeper understanding of fairness in software engineering and paves the way for the development of more equitable and socially responsible software systems.

cs.SE↗

arXiv2024

Hidden Figures in Software Engineering: A Replication Study Exploring Undergraduate Software Students' Awareness of Distinguished Scientists from Underrepresented Groups

Technology is a cornerstone of modern life, yet the software engineering field struggles to reflect the diversity of contemporary society. This lack of diversity and inclusivity within the software industry can be traced back to limited representation in software engineering academic settings, where students from underrepresented groups are often stigmatized despite the field's rich history of contributions from scientists from diverse backgrounds. Over the years, studies have revealed that women, LGBTQIA+ individuals, and Black students frequently encounter unwelcoming environments in software engineering programs. However, similar to other fields, increasing awareness of notable individuals from marginalized backgrounds could inspire students and foster a more inclusive environment. This study reports the findings from a replicated global survey with undergraduate software engineering students, exploring their knowledge of distinguished scientists from underrepresented groups. These findings show that students have limited awareness of these figures and their contributions, highlighting the need to improve diversity awareness and develop educational practices that celebrate the achievements of historically marginalized groups in software engineering. Index Terms-EDi in software engineering, software engineering education, diversity.

cs.SE↗

arXiv2024

Diversity in Software Engineering Education: Exploring Motivations, Influences, and Role Models Among Undergraduate Students

Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and professional success. Despite significant research on the exclusion experienced by students from underrepresented groups in SE education, there is limited understanding of the specific motivations, influences, and role models that drive underrepresented students to pursue and persist in the field. This study explores the motivations and influences shaping the career aspirations of students from underrepresented groups in SE, and it investigates how role models and mentorship impact their decisions to stay in the field. We conducted a cross-sectional survey with undergraduate SE students and related fields, focusing on their motivations, influences, and the impact of mentorship and role models on their career paths. We identified eight motivations for pursuing SE, with career advancement, technological enthusiasm, and personal growth being the most common. Family members, tech influencers, teachers, and friends were key influences, though 64\% of students reported no specific individual influence. Role models, particularly tech influencers and family members play a critical role in sustaining interest in the field, especially for underrepresented groups. This study provides insights into the varied motivations and influences that guide underrepresented students' decisions to pursue SE. It emphasizes the importance of role models and highlights the need for intersectional approaches to better support diversity in the field.

cs.SE↗

arXiv2024

Responsible AI in the Software Industry: A Practitioner-Centered Perspective

Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we investigated their methods, concerns, and strategies for addressing Responsible AI in software development. Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices. This scenario highlights gaps in current strategies and the need for more comprehensive frameworks to fully operationalize Responsible AI principles in software engineering.

cs.SE↗

arXiv2024

Integrating Positionality Statements in Empirical Software Engineering Research

Positionality statements are a reflective practice established in fields such as social sciences, where they enhance transparency, reflexivity, and ethical integrity by acknowledging how researchers identities, experiences, and perspectives may shape their work. This study aimed to investigate the understanding, usage, and potential value of positionality statements in software engineering research, particularly in studies focused on diversity and inclusion.

cs.SE↗

arXiv2024

Preliminary Insights on Industry Practices for Addressing Fairness Debt

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We used a qualitative research method, gathering insights from industry professionals through interviews and employing thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and issues with training data. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and provides a foundation for developing structured guidelines to manage fairness-related issues in AI systems.

cs.SE↗

arXiv2024

From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption

In today's world, we need to ensure that AI systems are fair and unbiased. Our study looked at tools designed to test the fairness of software to see if they are practical and easy for software developers to use. We found that while some tools are cost-effective and compatible with various programming environments, many are hard to use and lack detailed instructions. They also tend to focus on specific types of data, which limits their usefulness in real-world situations. Overall, current fairness testing tools need significant improvements to better support software developers in creating fair and equitable technology. We suggest that new tools should be user-friendly, well-documented, and flexible enough to handle different kinds of data, helping developers identify and fix biases early in the development process. This will lead to more trustworthy and fair software for everyone.

cs.SE↗

arXiv2024

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models.

cs.AI↗

arXiv2024

Remote Communication Trends Among Developers and Testers in Post-Pandemic Work Environments

The rapid adoption of remote and hybrid work models in response to the COVID-19 pandemic has brought significant changes to communication and coordination within software development teams, affecting how various activities are executed. Nowadays, these changes are shaping the new post-pandemic environments and continue to impact software teams. In this context, our study explores the characteristics and challenges of remote communication between software developers and software testers. We investigated how these professionals have adapted to the unique circumstances imposed by COVID-19, especially because many of them have now become permanent in the software industry. In this process, we explored their communication practices and interaction dynamics and how they potentially affect software evolution and quality. Our findings reveal that the transition to remote and hybrid work has resulted in notable changes in communication patterns and task coordination, which could potentially affect the overall quality of project deliverables. Additionally, we highlight the importance of adapting existing workflows, introducing new management practices, and investing in technology to facilitate remote interaction among developers and testers.

cs.SE↗

arXiv2024

The Role of Generative AI in Software Development Productivity: A Pilot Case Study

With software development increasingly reliant on innovative technologies, there is a growing interest in exploring the potential of generative AI tools to streamline processes and enhance productivity. In this scenario, this paper investigates the integration of generative AI tools within software development, focusing on understanding their uses, benefits, and challenges to software professionals, in particular, looking at aspects of productivity. Through a pilot case study involving software practitioners working in different roles, we gathered valuable experiences on the integration of generative AI tools into their daily work routines. Our findings reveal a generally positive perception of these tools in individual productivity while also highlighting the need to address identified limitations. Overall, our research sets the stage for further exploration into the evolving landscape of software development practices with the integration of generative AI tools.

cs.SE↗

arXiv2024

Exploring Hybrid Work Realities: A Case Study with Software Professionals From Underrepresented Groups

Context. In the post-pandemic era, software professionals resist returning to office routines, favoring the flexibility gained from remote work. Hybrid work structures, then, become popular within software companies, allowing them to choose not to work in the office every day, preserving flexibility, and creating several benefits, including an increase in the support for underrepresented groups in software development. Goal. We investigated how software professionals from underrepresented groups are experiencing post-pandemic hybrid work. In particular, we analyzed the experiences of neurodivergents, LGBTQIA+ individuals, and people with disabilities working in the software industry. Method. We conducted a case study focusing on the underrepresented groups within a well-established South American software company. Results. Hybrid work is preferred by software professionals from underrepresented groups in the post-pandemic era. Advantages include improved focus at home, personalized work setups, and accommodation for health treatments. Concerns arise about isolation and inadequate infrastructure support, highlighting the need for proactive organizational strategies. Conclusions. Hybrid work emerges as a promising strategy for fostering diversity and inclusion in software engineering, addressing past limitations of the traditional office environment.

cs.SE↗

arXiv2024

Paths to Testing: Why Women Enter and Remain in Software Testing?

Background. Women bring unique problem-solving skills to software development, often favoring a holistic approach and attention to detail. In software testing, precision and attention to detail are essential as professionals explore system functionalities to identify defects. Recognizing the alignment between these skills and women's strengths can derive strategies for enhancing diversity in software engineering. Goal. This study investigates the motivations behind women choosing careers in software testing, aiming to provide insights into their reasons for entering and remaining in the field. Method. This study used a cross-sectional survey methodology following established software engineering guidelines, collecting data from women in software testing to explore their motivations, experiences, and perspectives. Findings. The findings reveal that women enter software testing due to increased entry-level job opportunities, work-life balance, and even fewer gender stereotypes. Their motivations to stay include the impact of delivering high-quality software, continuous learning opportunities, and the challenges the activities bring to them. However, inclusiveness and career development in the field need improvement for sustained diversity. Conclusion. Preliminary yet significant, these findings offer interesting insights for researchers and practitioners towards the understanding of women's diverse motivations in software testing and how this understanding is important for fostering professional growth and creating a more inclusive and equitable industry landscape.

cs.SE↗

arXiv2024

Elevating Software Quality in Agile Environments: The Role of Testing Professionals in Unit Testing

Testing is an essential quality activity in the software development process. Usually, a software system is tested on several levels, starting with unit testing that checks the smallest parts of the code until acceptance testing, which is focused on the validations with the end-user. Historically, unit testing has been the domain of developers, who are responsible for ensuring the accuracy of their code. However, in agile environments, testing professionals play an integral role in various quality improvement initiatives throughout each development cycle. This paper explores the participation of test engineers in unit testing within an industrial context, employing a survey-based research methodology. Our findings demonstrate that testing professionals have the potential to strengthen unit testing by collaborating with developers to craft thorough test cases and fostering a culture of mutual learning and cooperation, ultimately contributing to increasing the overall quality of software projects.

cs.SE↗

arXiv2024

Exposing Algorithmic Discrimination and Its Consequences in Modern Society: Insights from a Scoping Study

Algorithmic discrimination is a condition that arises when data-driven software unfairly treats users based on attributes like ethnicity, race, gender, sexual orientation, religion, age, disability, or other personal characteristics. Nowadays, as machine learning gains popularity, cases of algorithmic discrimination are increasingly being reported in several contexts. This study delves into various studies published over the years reporting algorithmic discrimination. We aim to support software engineering researchers and practitioners in addressing this issue by discussing key characteristics of the problem

cs.SE↗

arXiv2024

Post-Pandemic Hybrid Work in Software Companies: Findings from an Industrial Case Study

Context. Software professionals learned from their experience during the pandemic that most of their work can be done remotely, and now software companies are expected to adopt hybrid work models to avoid the resignation of talented professionals who require more flexibility and work-life balance. However, hybrid work is a spectrum of flexible work arrangements, and currently, there are no well-established hybrid work configurations to be followed in the post-pandemic period. Goal. We investigated how software engineers are experiencing the post-pandemic hybrid work landscape, aiming to understand the factors that influence their choices between remote and in-office work. Method. We explored a large South American company by collecting quantitative and qualitative data from 545 software professionals who are currently navigating diverse hybrid work arrangements tailored to their individual and team requirements. Findings. Our study revealed an array of factors that significantly impact hybrid work within the software industry, including individual preferences, work-life balance, commute time, social interactions, productivity, and more. Team dynamics, project demands, client expectations, and organizational strategies also play an important role in shaping the complex landscape of hybrid work configurations in software engineering. Conclusions. In summary, the success of hybrid work models depends on balancing individual preferences, team dynamics, and organizational strategies. Our study demonstrated that, at present, there is no one-size-fits-all individual approach to hybrid work in the software industry.

cs.SE↗

arXiv2024

Charting a Path to Efficient Onboarding: The Role of Software Visualization

Background. Within the software industry, it is commonly estimated that software professionals invest a substantial portion of their work hours in the process of understanding existing systems. In this context, an ineffective technical onboarding process, which introduces newcomers to software under development, can result in a prolonged period for them to absorb the necessary knowledge required to become productive in their roles. Goal. The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools and how these tools are employed to facilitate the technical onboarding of new team members. Method. To address the research problem, we built upon the insights gained through the literature and embraced a sequential exploratory approach. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews. Findings. Our findings demonstrate a gap between the concept of software visualization and the practical use of onboarding tools and techniques. Overall, practitioners do not systematically incorporate software visualization tools into their technical onboarding processes due to a lack of conceptual understanding and awareness of their potential benefits. Conclusion. The software industry could benefit from standardized and evolving onboarding models, improved by incorporating software visualization techniques and tools to support program comprehension of newcomers in the software projects.

cs.SE↗

arXiv2024

Hidden Populations in Software Engineering: Challenges, Lessons Learned, and Opportunities

The growing emphasis on studying equity, diversity, and inclusion within software engineering has amplified the need to explore hidden populations within this field. Exploring hidden populations becomes important to obtain invaluable insights into the experiences, challenges, and perspectives of underrepresented groups in software engineering and, therefore, devise strategies to make the software industry more diverse. However, studying these hidden populations presents multifaceted challenges, including the complexities associated with identifying and engaging participants due to their marginalized status. In this paper, we discuss our experiences and lessons learned while conducting multiple studies involving hidden populations in software engineering. We emphasize the importance of recognizing and addressing these challenges within the software engineering research community to foster a more inclusive and comprehensive understanding of diverse populations of software professionals.

cs.SE↗