mlsanityData to 7 Oct 2026

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

arXiv 2511.06428 ↗First posted 9 Nov 2025cs.SE cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

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 for Data Analysis (STGT4DA) to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain developer flow, improve developer mental models, and foster entrepreneurship) and challenges (e.g., damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as actionable guidances into how mitigate these challenges. 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 77 references that are in the map. First posted on arXiv 9 Nov 2025.

Builds on

35 of 77 references.

References · 35

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  2. 2023Using an LLM to Help With Code Understandingcited by 222
  3. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  4. 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 163
  5. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 150
  6. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  7. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
  8. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  9. 2025How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inferencecited by 73
  10. 2023Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questionscited by 42
  11. 2025Agentic Software Engineering: Foundational Pillars and a Research Roadmapcited by 40
  12. 2023Navigating the Complexity of Generative AI Adoption in Software Engineeringcited by 32
23 more
  1. 2024Test-Driven Development for Code Generationcited by 32
  2. 2025Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Reviewcited by 28
  3. 2024Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactionscited by 20
  4. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  5. 2024Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Aheadcited by 17
  6. 2024An Empirical Study on Challenges for LLM Application Developerscited by 17
  7. 2024Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teamscited by 16
  8. 2024On the use of Large Language Models in Model-Driven Engineeringcited by 13
  9. 2023Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflowcited by 10
  10. 2025Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peerscited by 8
  11. 2025Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMscited by 8
  12. 2021Socio-Technical Grounded Theory for Software Engineeringcited by 7
  13. 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challengescited by 6
  14. 2024AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Studycited by 5
  15. 2024"Create a Fear of Missing Out" -- ChatGPT Implements Unsolicited Deceptive Designs in Generated Websites Without Warningcited by 5
  16. 2017Qualitative software engineering research -- reflections and guidelinescited by 4
  17. 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
  18. 2025The Role of Empathy in Software Engineering -- A Socio-Technical Grounded Theorycited by 2
  19. 2021Real World Scrum A Grounded Theory of Variations in Practicecited by 1
  20. 2022Dealing with Data Challenges when Delivering Data-Intensive Software Solutionscited by 1
  21. 2024Challenges, Adaptations, and Fringe Benefits of Conducting Software Engineering Research with Human Participants during the COVID-19 Pandemiccited by 1
  22. 2025Manifestations of Empathy in Software Engineering: How, Why, and When It Matterscited by 1
  23. 2025From Gains to Strains: Modeling Developer Burnout with GenAI Adoptioncited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.