Junior Software Developers' Perspectives on Adopting LLMs for Software Engineering: a Systematic Literature Review

• URL: http://arxiv.org/abs/2503.07556v1
• Date: Mon, 10 Mar 2025 17:25:24 GMT
• Title: Junior Software Developers' Perspectives on Adopting LLMs for Software Engineering: a Systematic Literature Review
• Authors: Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude,
• Abstract summary: This paper provides an overview of junior software developers' perspectives and use of Large Language Model-based tools for software engineering (LLM4SE)<n>We conducted a systematic literature review following guidelines by Kitchenham et al. on 56 primary studies.<n>Only 8.9% of the studies provide a clear definition for junior software developers, and there is no uniformity.
• Score: 17.22501688824729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many studies exploring the adoption of Large Language Model-based tools for software development by junior developers have emerged in recent years. These studies have sought to understand developers' perspectives about using those tools, a fundamental pillar for successfully adopting LLM-based tools in Software Engineering. The aim of this paper is to provide an overview of junior software developers' perspectives and use of LLM-based tools for software engineering (LLM4SE). We conducted a systematic literature review (SLR) following guidelines by Kitchenham et al. on 56 primary studies, applying the definition for junior software developers as software developers with equal or less than five years of experience, including Computer Science/Software Engineering students. We found that the majority of the studies focused on comprehending the different aspects of integrating AI tools in SE. Only 8.9\% of the studies provide a clear definition for junior software developers, and there is no uniformity. Searching for relevant information is the most common task using LLM tools. ChatGPT was the most common LLM tool present in the studies (and experiments). A majority of the studies (83.9\%) report both positive and negative perceptions about the impact of adopting LLM tools. We also found and categorised advantages, challenges, and recommendations regarding LLM adoption. Our results indicate that developers are using LLMs not just for code generation, but also to improve their development skills. Critically, they are not just experiencing the benefits of adopting LLM tools, but they are also aware of at least a few LLM limitations, such as the generation of wrong suggestions, potential data leaking, and AI hallucination. Our findings offer implications for software engineering researchers, educators, and developers.

Related papers

• LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters [3.4069804433026314]
  Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry.<n>Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development.
  arXiv  Detail & Related papers  (2025-03-06T22:27:05Z)
• Learning to Ask: When LLM Agents Meet Unclear Instruction [55.65312637965779]
  Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench. We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
  arXiv  Detail & Related papers  (2024-08-31T23:06:12Z)
• From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future [15.568939568441317]
  We investigate the current practice and solutions for large language models (LLMs) and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering.
  arXiv  Detail & Related papers  (2024-08-05T14:01:15Z)
• Tool Learning with Large Language Models: A Survey [60.733557487886635]
  Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
  arXiv  Detail & Related papers  (2024-05-28T08:01:26Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility. Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning. We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• An Exploratory Study on Upper-Level Computing Students' Use of Large Language Models as Tools in a Semester-Long Project [2.7325338323814328]
  The purpose of this study is to explore computing students' experiences and approaches to using LLMs during a semester-long software engineering project. We collected data from a senior-level software engineering course at Purdue University. We analyzed the data to identify themes related to students' usage patterns and learning outcomes.
  arXiv  Detail & Related papers  (2024-03-27T15:21:58Z)
• What Are Tools Anyway? A Survey from the Language Model Perspective [67.18843218893416]
  Language models (LMs) are powerful yet mostly for text generation tasks. We provide a unified definition of tools as external programs used by LMs. We empirically study the efficiency of various tooling methods.
  arXiv  Detail & Related papers  (2024-03-18T17:20:07Z)
• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error [54.954211216847135]
  Existing large language models (LLMs) only reach a correctness rate in the range of 30% to 60%. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE) STE orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory.
  arXiv  Detail & Related papers  (2024-03-07T18:50:51Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)
• An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project [1.433758865948252]
  Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s) In this paper, we analyze the AI-generated code, prompts used for code generation, and the human intervention levels to integrate the code into the code base. Our findings suggest that LLMs can play a crucial role in the early stages of software development.
  arXiv  Detail & Related papers  (2024-01-29T14:32:32Z)
• Calculating Originality of LLM Assisted Source Code [0.0]
  We propose a neural network-based tool to determine the original effort (and LLM's contribution) put by students in writing source codes. Our tool is motivated by minimum description length measures like Kolmogorov complexity.
  arXiv  Detail & Related papers  (2023-07-10T11:30:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.