AI POWERED CODE GENERATORS: OPPORTUNITIES AND RISKS FOR DEVELOPERS

Abstract

Artificial Intelligence (AI)-powered code generation represents a transformative shift in software engineering, offering significant improvements in development efficiency, code quality, and collaborative practices. This paper examines the opportunities and risks presented by AI-driven code generators, focusing on key techniques such as rule-based systems, machine learning, neural networks, Generative Adversarial Networks (GANs), and transformer models. Through empirical analysis and case studies, the study demonstrates that AI integration can reduce development time by 20–37.5%, enhance bug detection rates by up to 50%, and increase developer productivity by automating repetitive tasks and enabling focus on higher-level design and innovation. However, the adoption of AI-powered tools is not without challenges. Concerns related to training data quality, model interpretability, ethical implications, and potential biases necessitate careful consideration. Issues such as intellectual property, accountability for AI-generated code, and the evolving role of developers in an AI-augmented workflow are critically discussed. The study concludes that a balanced approach combining technological innovation with robust ethical guidelines and interdisciplinary collaboration is essential for harnessing the full potential of AI in software development.