软件工程中的氛围转变：评估AI主导的对话式编程的性能、认知与负责任采用
The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption
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中文总结 AI 辅助
本研究评估AI主导的对话式编程（Vibe Coding），发现其提升开发效率27%但降低代码质量，并提出负责任采用的三支柱框架。
中文摘要 AI 辅助
本研究评估了Vibe Coding（氛围编程），一种新兴的AI主导的对话式编程范式，它使开发者能够通过与大型语言模型的自然语言交互来生成软件。采用混合方法设计，本研究评估了与传统及AI辅助编码环境相比的性能效率、认知影响和负责任采用。三十名参与者，包括专业开发者和高级计算专业学生，在三种实验条件下完成了等效的编程任务。定量数据使用描述性统计和重复测量方差分析进行分析，而定性数据则通过主题分析进行检验。结果显示，氛围编程显著提高了开发效率，与传统编码相比任务完成时间减少了27%，与AI辅助编码相比减少了12%。然而，这些收益伴随着较低的可维护性指数和较高的安全漏洞，表明软件质量存在权衡。可用性结果获得了良好评级（SUS = 71.4），而认知负荷保持中等水平（NASA-TLX = 55.5），反映出语法努力的减少但语言推理的增加。主题分析确定了信任校准、失控感、认知适应和提示工程策略作为关键构念。值得注意的是，感知到的失控感与安全风险增加相关，这是由于AI生成输出的透明度和验证减少所致。基于这些发现，本研究提出了一个负责任采用的三支柱框架：人类与AI能力的混合集成、人类监督与透明问责，以及情境感知部署。总体而言，氛围编程提高了生产力，但需要关键监督，强化了其作为软件开发中变革性但过渡性范式的角色。
英文摘要
This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-language interaction with large language models. Using a mixed-methods design, the study assessed performance efficiency, cognitive implications, and responsible adoption in comparison with traditional and AI-assisted coding environments. Thirty participants, including professional developers and advanced computing students, completed equivalent programming tasks under three experimental conditions. Quantitative data were analyzed using descriptive statistics and repeated-measures ANOVA, while qualitative data were examined through thematic analysis. Results show that vibe coding significantly improved development efficiency, reducing task completion time by 27% compared with traditional coding and 12% compared with AI-assisted coding. However, these gains were accompanied by lower maintainability indices and higher security vulnerabilities, indicating trade-offs in software quality. Usability results yielded a good rating (SUS = 71.4), while cognitive workload remained moderate (NASA-TLX = 55.5), reflecting reduced syntactic effort but increased linguistic reasoning. Thematic analysis identified trust calibration, loss of control, cognitive adaptation, and prompt-engineering strategy as key constructs. Notably, perceived loss of control was associated with increased security risks due to reduced transparency and validation of AI-generated outputs. Based on these findings, the study proposes a three-pillar framework for responsible adoption: hybrid integration of human and AI capabilities, human oversight and transparent accountability, and context-aware deployment. Overall, vibe coding enhances productivity but requires critical oversight, reinforcing its role as a transformative yet transitional paradigm in software development.
发表机构
- Bukidnon State University（布基农州立大学）
机构由 AI 辅助整理，请以论文原文为准。
