Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
- URL: http://arxiv.org/abs/2511.06428v1
- Date: Sun, 09 Nov 2025 15:49:55 GMT
- Title: Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
- Authors: Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude,
- Abstract summary: We investigated how Large Language Models impact software development and how to manage the impact from a developer's perspective.<n>We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis.<n>We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs.
- Score: 18.50207872331241
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed socio-technical grounded theory (STGT) for data analysis to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as best practices on how to adopt LLMs. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.
Related papers
- Model-Assisted and Human-Guided: Perceptions and Practices of Software Professionals Using LLMs for Coding [2.198430261120653]
Large Language Models have quickly become a central component of modern software development.<n>This paper presents preliminary findings from a global survey of 131 software practitioners.
arXiv Detail & Related papers (2025-10-10T06:59:56Z) - Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review [17.22501688824729]
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.
arXiv Detail & Related papers (2025-03-10T17:25:24Z) - 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) - LLMs: A Game-Changer for Software Engineers? [0.0]
Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications.
Their potential to revolutionize software development has captivated the software engineering (SE) community.
This paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers.
arXiv Detail & Related papers (2024-11-01T17:14:37Z) - 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.<n>In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance.<n>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) - CIBench: Evaluating Your LLMs with a Code Interpreter Plugin [68.95137938214862]
We propose an interactive evaluation framework, named CIBench, to comprehensively assess LLMs' ability to utilize code interpreters for data science tasks.
The evaluation dataset is constructed using an LLM-human cooperative approach and simulates an authentic workflow by leveraging consecutive and interactive IPython sessions.
We conduct extensive experiments to analyze the ability of 24 LLMs on CIBench and provide valuable insights for future LLMs in code interpreter utilization.
arXiv Detail & Related papers (2024-07-15T07:43:55Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
We explore machine unlearning in the domain of large language models (LLMs)<n>This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
arXiv Detail & Related papers (2024-02-13T20:51:58Z) - Large Language Models: A Survey [66.39828929831017]
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks.<n>LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
arXiv Detail & Related papers (2024-02-09T05:37:09Z) - 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) - Survey on Factuality in Large Language Models: Knowledge, Retrieval and
Domain-Specificity [61.54815512469125]
This survey addresses the crucial issue of factuality in Large Language Models (LLMs)
As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
arXiv Detail & Related papers (2023-10-11T14:18:03Z) - A Survey on Evaluation of Large Language Models [87.60417393701331]
Large language models (LLMs) are gaining increasing popularity in both academia and industry.
This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
arXiv Detail & Related papers (2023-07-06T16:28:35Z)
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.