一句话：系统综述 AI 编程助手对开发者生产力的影响，揭示 187 个碎片化指标并构建多维可比证据基础。
本文针对 AI 编程助手生产力证据难以横向比较的问题，进行分阶段系统性文献综述：先综合明确考察生产力的研究，再以 SPACE 框架扩展至满意度、心理与福祉等相关结果。研究分析生产力操作化方式、混杂因素与效应分布。结果识别出 187 个独特指标，但单项研究平均仅用 5 个，证据基础广泛却高度碎片化；显式研究偏重时间、产出与制品代理，SPACE 语料更突出满意度与福祉。报告效应多为正向，但感知与倾向性发现强于客观或统计评估。总体而言，AI 编程助手可能支持生产力，但尚不足以得出简单、脱离语境的结论。
AI coding assistants are increasingly embedded in software engineering practice, yet the literature still offers no cumulative, easily interpretable picture of their effects on software developer productivity. The central problem is not a lack of studies, but a lack of comparability: prior work operationalizes productivity through heterogeneous markers, settings, and reporting styles, making cross-study conclusions difficult. To address this, we conduct a staged systematic literature review. In the first stage, we synthesize studies that explicitly examine developer productivity. In the second, we extend the scope to productivity-related developer outcomes using SPACE as a theory-informed lens, capturing human-centered evidence that explicit productivity studies often omit. Across both stages, we analyze how productivity is operationalized, which confounding factors are discussed, and how reported effects are distributed. We identify 187 unique productivity and productivity-related markers, while individual studies typically only use five, revealing a broad but highly fragmented evidence base. Explicit productivity research is dominated by time-, output-, and artifact-oriented proxies, whereas the broader SPACE-aligned corpus makes satisfaction, psychological aspects, and well-being substantially more visible. Reported effects are often positive, but this pattern is stronger in perceived and tendency-based findings than in objective or statistically evaluated ones. Overall, the literature suggests that AI coding assistants can support productivity, but current evidence does not yet support simple, context-independent conclusions. The key contribution of this review is therefore a structured basis for making productivity evidence more interpretable, multidimensional, and comparable across studies.
@inproceedings{paperbot12454,
title = {Fragmented Markers, Mixed Results: A Systematic Review of AI Coding Assistants and Developer Productivity},
author = {Annemarie Wittig and Alina Mailach and Norbert Siegmund},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834386}
}