# Generative AI and the Transformation of Software Development Practices

arXiv: https://arxiv.org/abs/2510.10819 · HTML: https://mlsanity.com/paper/2510.10819/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality)](https://mlsanity.com/paper/2510.03331/) (2025)

## Built on by (most cited first, 3 of 3)

- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- ["Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI](https://mlsanity.com/paper/2604.06342/) (2026)
- [Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces](https://mlsanity.com/paper/2607.05127/) (2026)

## Abstract (arXiv)

Generative AI is reshaping how software is designed, written, and maintained. Advances in large language models (LLMs) are enabling new development styles - from chat-oriented programming and 'vibe coding' to agentic programming - that can accelerate productivity and broaden access. This paper examines how AI-assisted techniques are changing software engineering practice, and the related issues of trust, accountability, and shifting skills. We survey iterative chat-based development, multi-agent systems, dynamic prompt orchestration, and integration via the Model Context Protocol (MCP). Using case studies and industry data, we outline both the opportunities (faster cycles, democratized coding) and the challenges (model reliability and cost) of applying generative AI to coding. We describe new roles, skills, and best practices for using AI in a responsible and effective way.