← 研踪 PaperTrailarXiv:2605.23135 · v2
The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
首次提交 2026-05-22 09:18
版本更新 2026-11-04 03:02
雷达观测 2026-11-05 11:11
摘要
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 found a 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. This construct is broader than the extra review overhead that has been previously reported in literature since it encompasses directing and shaping AI output both before and during creation. We also identified a productivity-experience paradox: consistently positive productivity perceptions alongside eroding developer experience. These findings suggest that AI coding assistants may be reshaping both the nature of software engineering work and how engineers experience it.
作者备注：Revised version. Accepted for publication in the ACM Transaction on Software Engineering and Methodology
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v2 · 2026-11-04 03:02 · 最新观测版本
The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
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 found a 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. This construct is broader than the extra review overhead that has been previously reported in literature since it encompasses directing and shaping AI output both before and during creation. We also identified a productivity-experience paradox: consistently positive productivity perceptions alongside eroding developer experience. These findings suggest that AI coding assistants may be reshaping both the nature of software engineering work and how engineers experience it.
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