发表机构
CESAR School; UFRPE; University of Calgary; University of Hawaii at Manoa（CESAR学院; 巴西联邦农村大学; 卡尔加里大学; 夏威夷大学马诺阿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过调查75名初级软件工程师，发现LLM已融入日常开发活动，其有效使用依赖传统工程能力与批判性思维，并指出职场期望与大学教育存在差距，为软件工程教育和培训提供启示。
AI 中文摘要
尽管大型语言模型在专业软件工程领域被迅速采用，但关于早期职业专业人士在进入行业期间如何发展有效的AI辅助工作实践的研究仍然有限。我们报告了一项混合方法调查的结果，该调查涉及75名在日常工作中积极使用LLM支持工具的初级软件工程师。我们的结果显示，LLM已嵌入到常规软件工程活动中，包括编码、调试、测试、文档编写和问题解决。有效使用依赖于传统软件工程能力，如调试、测试和架构推理，以及批判性思维、输出验证、提示工程和持续的人工监督。我们还发现了工作场所期望与大学教育之间的差距，大多数参与者报告称，在实践性LLM使用方面接受的正式教育有限。这些发现对软件工程教育、组织入职培训以及AI辅助软件工程中的劳动力发展具有启示意义。
英文摘要
Despite the rapid adoption of Large Language Models in professional software engineering, limited research has investigated how early career professionals develop effective AI assisted work practices during their transition into industry. We report findings from a mixed methods survey with 75 novice software engineers who actively use LLM supported tools in their daily work. Our results show that LLMs are embedded in routine software engineering activities, including coding, debugging, testing, documentation, and problem solving. Effective use depends on traditional software engineering competencies, such as debugging, testing, and architectural reasoning, together with critical thinking, output verification, prompt engineering, and continuous human oversight. We also identify a gap between workplace expectations and university preparation, with most participants reporting limited formal education on practical LLM use. These findings have implications for software engineering education, organizational onboarding, and workforce development in AI assisted software engineering.
