GenAI显著提升开发中设计、编码与文档效率，但需警惕盲目使用风险。
The State of Generative AI in Software Development: Insights from Literature and a Developer Survey
- 结合文献综述与65名开发者调研，分析GenAI在开发全流程影响。
- 超70%开发者称生成代码和文档时间减半，79%每日使用GenAI。
- 适合关注AI提效与治理的开发者、技术管理者阅读。
生成式人工智能（GenAI）正迅速改变软件工程，但现有研究多局限于开发流程中的单一任务。本研究整合系统性文献综述与对65名软件开发者的调查，发现GenAI在设计、实现、测试和文档环节影响最大，超过70%的开发者报告重复性任务（如样板代码和文档）耗时至少减少一半。79%的受访者每天使用GenAI，更偏好浏览器端大语言模型而非集成于开发环境的方案。治理机制逐步成熟，三分之二的组织已建立正式或非正式指导规范。相比之下，规划与需求分析等早期阶段报告收益较低。总体而言，GenAI推动价值创造从常规编码转向规格质量、架构推理与人工监督；但需防范无批判采纳、技能退化与技术债累积等问题，亟需健全治理与人机协同机制。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (GenAI) rapidly transforms software engineering, yet existing research remains fragmented across individual tasks in the Software Development Lifecycle. This study integrates a systematic literature review with a survey of 65 software developers. The results show that GenAI exerts its highest impact in design, implementation, testing, and documentation, where over 70 % of developers report at least halving the time for boilerplate and documentation tasks. 79 % of survey respondents use GenAI daily, preferring browser-based Large Language Models over alternatives integrated directly in their development environment. Governance is maturing, with two-thirds of organizations maintaining formal or informal guidelines. In contrast, early SDLC phases such as planning and requirements analysis show markedly lower reported benefits. In a nutshell, GenAI shifts value creation from routine coding toward specification quality, architectural reasoning, and oversight, while risks such as uncritical adoption, skill erosion, and technical debt require robust governance and human-in-the-loop mechanisms.
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