The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study

2026-05-21

AI Summary

This study investigates the long-term impact of AI programming assistants on software engineers’ task focus, development experience, and productivity. Employing a longitudinal mixed-methods design, it tracks professional developers over six months through two waves of surveys and paired-sample analyses, uncovering profound shifts in work practices. The research introduces the novel concept of “supervisory engineering work,” revealing that 82% of participants reduced time spent coding and redirected their focus toward verification tasks. While 84% consistently reported enhanced productivity, the proportion experiencing negative aspects of their workflow rose from 14% to 27%, with diminished flow states despite improved feedback loops—highlighting a paradoxical coexistence of productivity gains and deteriorating experiential quality.

Abstract

AI coding assistants have become prolific in recent years. Through a longitudinal mixed-methods investigation, we examined how professional software engineers perceive the effects of AI coding assistants in regard to task focus, developer experience, and productivity. Two questionnaires were administered six months apart, yielding 158 eligible participants at the first time point, 101 at the second, and a matched longitudinal cohort of 95. Participants reported spending less time on most development tasks, with 82% reporting less on writing code. We find broader shift in focus from creation to verification activities. We propose a new category of work we term supervisory engineering work, encompassing the direction, evaluation, and correction of AI output. We also identified a productivity-experience paradox: productivity perceptions held stable, with 84% reporting improvement at both time points, yet among matched participants, the proportion reporting worsened developer experience in at least one dimension nearly doubled from 14% to 27%, with flow state and cognitive load eroding while feedback loops improved. These findings suggest that AI coding assistants are impacting both the nature of software engineering work and how engineers experience it.

Problem

• AI coding assistants
• software engineering
• developer experience
• productivity
• supervisory engineering

Innovation

• supervisory engineering work
• productivity-experience paradox
• AI coding assistants
• longitudinal study
• developer experience