Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable of code generation may support scientific programmers, but user studies indicate risks of over-reliance, particularly among inexperienced users. We surveyed 868 scientists who program, examining adoption patterns, tool preferences, and factors associated with perceived productivity. Adoption is highest among students and less experienced programmers, with variation across fields. Scientific programmers overwhelmingly prefer general-purpose conversational interfaces like ChatGPT over developer-specific tools. Both inexperience and limited use of development practices (like testing, code review, and version control) are associated with greater perceived productivity-but these factors interact, suggesting formal practices may partially compensate for inexperience. The strongest predictor of perceived productivity is the number of lines of generated code typically accepted at once. These findings suggest scientific programmers using generative AI may gauge productivity by code generation rather than validation, raising concerns about research code integrity.

翻译：编程是现代科学研究不可或缺的组成部分，然而大多数科学家表示，他们未接受过充分训练以满足工作所需的软件开发能力。具备代码生成能力的生成式AI工具可能为科学编程人员提供支持，但用户研究表明存在过度依赖的风险，特别是在经验不足的用户中。我们对868名从事编程的科学家进行了调查，考察了工具采用模式、偏好选择以及与感知生产力相关的因素。学生群体和编程经验较少的科研人员采用率最高，且存在跨学科差异。科学编程人员明显更倾向于使用ChatGPT等通用对话式界面，而非专为开发者设计的工具。经验不足与开发实践（如测试、代码审查和版本控制）的有限使用均与更高的感知生产力相关——但这些因素存在交互作用，表明规范化的开发实践可能部分弥补经验缺失。感知生产力的最强预测因子是一次性接受的生成代码行数。这些发现表明，使用生成式AI的科学编程人员可能通过代码生成量而非验证质量来评估生产力，这引发了关于研究代码完整性的担忧。