Generative AI and the Transformation of Software Development Practices
Vivek Acharya
TL;DR
This survey tackles how generative AI transforms software development by examining paradigms like CHOP, vibe coding, and agentic programming, supported by MCP and multi-agent clusters. It analyzes trust, accountability, and cost considerations, and articulates a comprehensive framework for AI-enabled development, including governance, context-aware interfaces, and skill evolution. The findings indicate substantial productivity gains and new workflows, but also highlight risks from model reliability, code quality, and economic compute costs. Collectively, the work emphasizes a future where developers act as curators and overseers of AI-powered tools, with standardized contexts and collaborative agent systems driving faster, more accessible software creation while demanding robust best practices and continuous learning.
Abstract
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.