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

arXiv: https://arxiv.org/abs/2511.06428 · HTML: https://mlsanity.com/paper/2511.06428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 77 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 30 of 35)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference](https://mlsanity.com/paper/2505.09598/) (2025)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions](https://mlsanity.com/paper/2308.02312/) (2023)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [Test-Driven Development for Code Generation](https://mlsanity.com/paper/2402.13521/) (2024)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review](https://mlsanity.com/paper/2510.00328/) (2025)
- [Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions](https://mlsanity.com/paper/2402.11364/) (2024)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [An Empirical Study on Challenges for LLM Application Developers](https://mlsanity.com/paper/2408.05002/) (2024)
- [Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead](https://mlsanity.com/paper/2404.04566/) (2024)
- [Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams](https://mlsanity.com/paper/2402.17721/) (2024)
- [On the use of Large Language Models in Model-Driven Engineering](https://mlsanity.com/paper/2410.17370/) (2024)
- [Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow](https://mlsanity.com/paper/2311.01020/) (2023)
- [Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers](https://mlsanity.com/paper/2501.02092/) (2025)
- [Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs](https://mlsanity.com/paper/2502.15963/) (2025)
- [Socio-Technical Grounded Theory for Software Engineering](https://mlsanity.com/paper/2103.14235/) (2021)
- [Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges](https://mlsanity.com/paper/2307.10057/) (2023)
- [AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study](https://mlsanity.com/paper/2406.17325/) (2024)
- ["Create a Fear of Missing Out" -- ChatGPT Implements Unsolicited Deceptive Designs in Generated Websites Without Warning](https://mlsanity.com/paper/2411.03108/) (2024)
- [Qualitative software engineering research -- reflections and guidelines](https://mlsanity.com/paper/1712.08341/) (2017)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [The Role of Empathy in Software Engineering -- A Socio-Technical Grounded Theory](https://mlsanity.com/paper/2504.13002/) (2025)

## Built on by (most cited first, 2 of 2)

- [Agentic AI in Industry: Adoption Level and Deployment Barriers](https://mlsanity.com/paper/2605.14675/) (2026)
- [Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development](https://mlsanity.com/paper/2609.30863/) (2026)

## 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.