Factors Impacting Developer Efficiency: Results from an Adaptive Longitudinal Study
2026-08-17 • Software Engineering
Software Engineering
AI summaryⓘ
The authors studied what slows down software developers over time in a consulting setting, checking repeatedly with surveys and interviews. They found that waiting on others and organizational issues stayed major problems, while technical skill gaps got smaller as developers learned. Midway through, challenges related to using generative AI tools appeared and became a top concern. Their study shows that developer efficiency changes over time and measuring it once is not enough. They suggest organizations focus on reducing external hold-ups, improving communication, and supporting developers in using AI tools.
developer efficiencylongitudinal studyorganizational dependenciestechnical knowledge gapsgenerative AIAdaptive Developer Efficiency Monitoring Method (ADEMM)consulting software developmentsemi-structured interviewsmixed-methods researchbottlenecks
Authors
Danilo Ribeiro, Breno Alves, Gabriel Souza, César França, Alberto Souza
Abstract
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.