Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review

• URL: http://arxiv.org/abs/2503.07556v2
• Date: Fri, 01 Aug 2025 07:38:59 GMT
• Title: Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review
• Authors: Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude,
• Abstract summary: We conducted a systematic literature review of 80 studies published between April 2022 and June 2025 to answer four research questions (RQs)<n>In RQ1, we categorised the study motivations and methodological approaches.<n>In RQ2, we identified the software development tasks for which novice developers use LLMs.<n>In RQ3, we categorised the advantages, challenges, and recommendations discussed in the studies.
• Score: 17.22501688824729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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