ADEMM: A Longitudinal Method for Monitoring Developer Efficiency in Industry
2026-08-17 • Software Engineering
Software Engineering
AI summaryⓘ
The authors developed ADEMM, a method to keep track of how well software developers work over time, especially when their employers don’t directly manage them. They used surveys and interviews repeatedly with 27 developers to improve this approach. ADEMM helps gather different types of information and adjusts the questions based on what’s useful, making it easier to understand changes in productivity. The authors found three key rules for using ADEMM effectively, focusing on working closely with decision-makers and balancing different data types. This method can be used by organizations to monitor developer efficiency more flexibly and accurately.
Developer efficiencyLongitudinal studyAdaptive monitoringMixed methodsSurvey designDesign Science ResearchAction Design ResearchSoftware developmentProductivity measurementQualitative data
Authors
Danilo Ribeiro, Breno Alves, Gabriel Souza, César França, Alberto Souza
Abstract
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.