Walking the Tightrope of LLMs for Software Development: A Practitioners'Perspective

2025-11-09

AI Summary

This study investigates the multidimensional trade-offs introduced by large language models (LLMs) in software development. Drawing on iterative, in-depth interviews with 22 practitioners, we apply socio-technical grounded theory (STGT) to conduct systematic qualitative coding and analysis. We identify both positive impacts—such as enhanced development fluency and heightened entrepreneurial intent—and negative risks—including erosion of professional identity among developers and threats to technical reputation—across individual, team, organizational, and societal levels. Our work innovatively uncovers LLMs’ profound effects on developer cognition and organizational culture. Building on these insights, we propose the first practice-oriented LLM adoption framework, explicitly designed to support context-sensitive, actionable decision-making for team leads and IT managers. The framework bridges theoretical understanding with operational guidance, addressing critical gaps in current LLM governance and integration strategies.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Investigating how LLMs impact software development from practitioners' perspective
• Identifying benefits and disadvantages of LLMs across individual, team, and organizational levels
• Presenting trade-offs and best practices for adopting LLMs in software development

Innovation

• Conducted interviews with software practitioners across three rounds
• Employed socio-technical grounded theory for data analysis
• Identified benefits and disadvantages of LLMs at multiple levels