ADEMM: A Longitudinal Method for Monitoring Developer Efficiency in Industry

2026-08-17

AI Summary

This study addresses the challenge of monitoring the dynamic evolution of developer productivity barriers in non-direct employment contexts by proposing ADEMM, an adaptive longitudinal monitoring method. Grounded in design science and action research, this approach employs iterative mixed-methods design to establish three core principles: operability prioritization, hybrid data collection, and dynamic item adjustment. ADEMM effectively balances data comparability, contextual sensitivity, and practical utility, enabling continuous data gathering and real-time tool adaptation within indirect employment settings. Consequently, it facilitates precise tracking of barrier evolution trajectories, offering a novel paradigm for effectiveness research in complex workforce arrangements.

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.

Problem

• Developer Efficiency
• Longitudinal Monitoring
• Adaptive Method
• Non-direct Employment Context

Innovation

• Adaptive Longitudinal Monitoring
• Developer Efficiency
• Mixed-Methods Design
• Iterative Instrument Redesign
• Design Science Research