arXiv subjects

Alberto Souza

Publications and source records attributed to Alberto Souza.

3 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↗

arXiv2021

Changing Software Engineers' Self-Efficacy with Bootcamps:A Research Proposal

In several areas of knowledge, self-efficacy is related to the perfomance of individuals, including in Software Engineering. However,it is not clear how self-efficacy can be modified in training conducted by the industry. Furthermore, we still do not understand how self-efficacy can impact an individual's team and career in the industry. This lack of understanding can negatively impact how companies and individuals perceive the importance of self-efficacy in the field. Therefore, We present a research proposal that aims to understand the relationship between self-efficacy and training in Software Engineering. Moreover, we look to understand the role of self-efficacy at Software Development industry. We propose a longitudinal case study with software engineers at Zup Innovation that participating of our bootcamp training. We expect to collect data to support our assumptions that self-efficacy can be related to training in Software Engineering. The other assumption is that self-efficacy at the beginning of training is higher than the middle, and that self-efficacy at the end of training is higher than the middle. We expect that the study proposed in this article will motivate a discussion about self-efficacy and the importance of training employers in the industry of software development.

cs.SE↗