梳理新手开发者用大模型编程的痛点与需求，为教学和工具设计提供依据。
Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review
- 系统分析80篇论文，归纳新手用大模型的常见任务和场景。
- 发现新手用大模型主要提升编码效率，但存在依赖过强、信任不足等问题。
- 适合教育者、工具开发者和研究者参考，推动更适配新手的AI辅助开发方案。
随着大语言模型（LLMs）的兴起，近年来大量研究聚焦于探索新手开发者（包括计算机科学/软件工程专业学生及从业两年内的初学者）采用基于大模型的开发工具的现状。这些研究旨在理解新手对这类工具的看法，这是实现大模型在软件工程中成功应用的关键。为系统性地收集并总结相关研究，本文遵循Kitchenham等人提出的指南，对2022年4月至2025年6月间发表的80篇原始研究进行了系统文献综述（SLR），回答四个研究问题（RQs）。RQ1中，我们对研究动机与方法学进行了分类；RQ2中，识别了新手使用大模型的具体软件开发任务；RQ3中，对优势、挑战及建议进行了归类分析；最后，在RQ4中讨论了原始研究中的局限性与未来研究方向。本文还提出了未来工作建议，并对软件工程研究人员、教育者和开发者具有启示意义。研究成果已公开发布于https://github.com/Samuellucas97/SupplementaryInfoPackage-SLR。
原文摘要 · Abstract (English)
Following the rise of large language models (LLMs), many studies have emerged in recent years focusing on exploring the adoption of LLM-based tools for software development by novice developers: computer science/software engineering students and early-career industry developers with two years or less of professional experience. These studies have sought to understand the perspectives of novice developers on using these tools, a critical aspect of the successful adoption of LLMs in software engineering. To systematically collect and summarise these studies, we conducted a systematic literature review (SLR) following the guidelines by Kitchenham et al. on 80 primary studies published between April 2022 and June 2025 to answer four research questions (RQs). In answering RQ1, we categorised the study motivations and methodological approaches. In RQ2, we identified the software development tasks for which novice developers use LLMs. In RQ3, we categorised the advantages, challenges, and recommendations discussed in the studies. Finally, we discuss the study limitations and future research needs suggested in the primary studies in answering RQ4. Throughout the paper, we also indicate directions for future work and implications for software engineering researchers, educators, and developers. Our research artifacts are publicly available at https://github.com/Samuellucas97/SupplementaryInfoPackage-SLR.
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