Intent-Driven Code Synthesis: Redefining Software Development with Transformers

The future of software development is being redefined by AI-powered assistants capable of generating code from natural language intent. This paper investigates the evolution of transformer-based development models designed to understand plain-text prompts, learn structural programming logic, and produce accurate, executable code across diverse languages. Using benchmarks such as HumanEval, MBPP, CodeXGLUE, and CONCODE, we assess the capabilities and limitations of these models in practical code synthesis scenarios. Beyond boosting developer productivity, we frame this shift as a foundational transformationenabling rapid prototyping, inclusive development for non-programmers, and intelligent integration within the engineering lifecycle. We detail architectural pipelines, IDE and CI/CD deployment strategies, and highlight emerging risks including semantic bugs, licensing conflicts, and trust deficits. Furthermore, we outline validation techniques and governance practices to ensure safe and ethical model usage. This paper presents a comprehensive roadmap for building transparent, adaptive, and trustworthy AI systems that act as collaborative partners in software creationenhancing human ingenuity while maintaining code quality, reliability, and accountability.