Many software development platforms now support LLM-driven programming, or "vibe coding", a technique that allows one to specify programs in natural language and iterate from observed behavior, all without directly editing source code. While its adoption is accelerating, little is known about which skills best predict success in this workflow. We report a preregistered cross-sectional study with tertiary-level students (N = 100) who completed measures of computer-science achievement, domain-general cognitive skills, written-communication proficiency, and a vibe-coding assessment. Tasks were curated via an eight-expert consensus process and executed in a purpose-built, vibe-coding environment that mirrors commercial tools while enabling controlled evaluation. We find that both writing skill and CS achievement are significant predictors of vibe-coding performance, and that CS achievement remains a significant predictor after controlling for domain-general cognitive skills. The results may inform tool and curriculum design, including when to emphasize prompt-writing versus CS fundamentals to support future software creators.

翻译：当前许多软件开发平台已支持LLM驱动的编程（即"氛围编码"）——这种技术允许开发者使用自然语言描述程序需求，并通过观察运行行为进行迭代优化，全程无需直接编辑源代码。尽管该工作流正加速普及，但何种技能最能预测其应用成效尚不明确。本研究通过预注册的横断面实验，对100名高等教育阶段学生进行了计算机科学素养、领域通用认知能力、书面交流能力及氛围编码能力的综合测评。所有任务均通过八位专家共识流程设计，并在模拟商业工具功能同时支持受控评估的专用氛围编码环境中执行。研究发现：写作技能与计算机科学素养均为氛围编码表现的重要预测因子，且在控制领域通用认知能力后，计算机科学素养仍保持显著预测效力。本研究成果可为工具设计与课程开发提供参考，有助于明确在培养未来软件开发者过程中，何时应侧重提示词撰写训练，何时需夯实计算机科学基础。