arXiv subjects

César França

Publications and source records attributed to César França.

6 recordsLinked to original sources

arXiv2026

ADEMM: A Longitudinal Method for Monitoring Developer Efficiency in Industry

Context: Developer efficiency is influenced by technical, organizational, cognitive, and communication-related factors. However, most studies rely on one-time assessments or fixed instruments, limiting the ability to monitor how barriers emerge and change over time, especially in consulting and professional education contexts. Objective: This study proposes and evaluates the Adaptive Developer Efficiency Monitoring Method (ADEMM), an adaptive longitudinal method for monitoring developer efficiency when the monitoring organization does not directly employ the developers. Method: Following Design Science Research and Action Design Research, we conducted a mixed-method longitudinal study with 27 software developers over twelve survey cycles. ADEMM was designed and refined through five iterative cycles, combining recurring surveys, 18 semi-structured interviews, and joint evaluation with a problem owner. Results: The study resulted in ADEMM, a method that supports continuous data collection, mixed-methods integration, and iterative redesign of monitoring instruments. The evaluation produced three design principles: prioritization with the problem owner based on actionability, combination of closed and open data collection, and adaptation of items based on low variance and emerging qualitative signals. Conclusions: ADEMM provides a transferable approach for adaptive longitudinal monitoring of developer efficiency. It helps balance comparability, contextual sensitivity, and practical utility in environments where organizations need to support developers without directly controlling their work contexts.

cs.SE↗

arXiv2026

Factors Impacting Developer Efficiency: Results from an Adaptive Longitudinal Study

Context: Developer efficiency is driven by technical, organizational, and personal factors, yet few longitudinal studies explore how these factors evolve over time. Objective: This study investigates the primary factors hindering the perceived efficiency of developers in a consulting and professional development context, analyzing how these factors vary across recurring data collection cycles and how they are described qualitatively. Method: We conducted a mixed-methods longitudinal case study applying the Adaptive Developer Efficiency Monitoring Method (ADEMM) to 27 external software developers, combining twelve waves of periodic surveys with eighteen semi-structured interviews, analyzed through statistical and thematic analysis. Results: The most frequent bottlenecks were organizational dependencies and waiting for external validation, which stayed structurally stable, followed by technical knowledge gaps, which declined as developers adapted. A generative AI usage barrier emerged qualitatively nine waves into the study, was incorporated into the survey instrument, and became the most frequently coded interview theme. Interviews corroborated the quantitative findings, with insufficient requirements documentation and organizational dependencies as the most recurrent themes alongside AI-related challenges. Conclusions: Perceived developer efficiency is highly dynamic and cannot be accurately captured through a single cross-sectional measurement. Adaptive monitoring via ADEMM identified an emerging factor, generative AI usage barriers, that a fixed instrument would have missed, and informed a concrete organizational intervention during the study. For organizations managing external developers, actions should target external dependencies, communication channels, and developers' evolving use of AI tools.

cs.SE↗

arXiv2023

Exploring the intersection of Generative AI and Software Development

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. Spanning from project management to support and updates, we meticulously map the demands of each development stage and unveil the potential of generative AI in addressing them. Techniques such as zero-shot prompting, self-consistency, and multimodal chain-of-thought are explored, showcasing their unique capabilities in enhancing generative AI models. The significance of vector embeddings, context, plugins, tools, and code assistants is underscored, emphasizing their role in capturing semantic information and amplifying generative AI capabilities. Looking ahead, this intersection promises to elevate productivity, improve code quality, and streamline the software development process. This whitepaper serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering, fostering innovation and collaboration for a qualitative leap in the efficiency and effectiveness of software development.

cs.SE↗

arXiv2023

Navigating the Path of Women in Software Engineering: From Academia to Industry

Context. Women remain significantly underrepresented in software engineering, leading to a lasting gender gap in the software industry. This disparity starts in education and extends into the industry, causing challenges such as hostile work environments and unequal opportunities. Addressing these issues is crucial for fostering an inclusive and diverse software engineering workforce. Aim. This study aims to enhance the literature on women in software engineering, exploring their journey from academia to industry and discussing perspectives, challenges, and support. We focus on Brazilian women to extend existing research, which has largely focused on North American and European contexts. Method. In this study, we conducted a cross-sectional survey, collecting both quantitative and qualitative data, focusing on women's experiences in software engineering to explore their journey from university to the software industry. Findings. Our findings highlight persistent challenges faced by women in software engineering, including gender bias, harassment, work-life imbalance, undervaluation, low sense of belonging, and impostor syndrome. These difficulties commonly emerge from university experiences and continue to affect women throughout their entire careers. Conclusion. In summary, our study identifies systemic challenges in women's software engineering journey, emphasizing the need for organizational commitment to address these issues. We provide actionable recommendations for practitioners.

cs.SE↗

arXiv2023

AI empowering research: 10 ways how science can benefit from AI

This article explores the transformative impact of artificial intelligence (AI) on scientific research. It highlights ten ways in which AI is revolutionizing the work of scientists, including powerful referencing tools, improved understanding of research problems, enhanced research question generation, optimized research design, stub data generation, data transformation, advanced data analysis, and AI-assisted reporting. While AI offers numerous benefits, challenges such as bias, privacy concerns, and the need for human-AI collaboration must be considered. The article emphasizes that AI can augment human creativity in science but not replace it.

cs.GL↗

arXiv2023

Understanding Self-Efficacy in the Context of Software Engineering: A Qualitative Study in the Industry

CONTEXT: Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. In Software Engineering, it has mainly been studied in the academic context, presenting results similar to other areas of knowledge. However, it is also important to understand its impact in the industrial context. OBJECTIVE: Therefore, this study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy in software engineers and how self-efficacy can impact the work-day of software engineers. METHOD: A qualitative research was conducted using semi-structured questionnaires with 31 interviewees from a software development company located in Brazil. The interviewees participated in a Bootcamp and were later assigned to software development teams. Thematic analysis was used to analyze the data. RESULTS: In the perception of the interviewees, 21 signs were found that are related to people with high and low self-efficacy. These signs were divided into two dimensions: social and cognitive. Also, 18 situations were found that can lead to an increase or decrease of self-efficacy of software engineers. Finally, 12 factors were mentioned that can impact software development teams. CONCLUSION: This work evidences a set of behavioral signs that can help team leaders to better perceive the self-efficacy of their members. It also presents a set of situations that both leaders and individuals can use to improve their self-efficacy in the development context, and finally, factors that can be impacted by self-efficacy in the software development context are also presented. Finally, this work emphasizes the importance of understanding self-efficacy in the industrial context.

cs.SE↗