Towards intelligent software development: An AI-based code generation tool

Abstract

The increasing scale and structural complexity of contemporary software applications have intensified the need for development tools that can support faster implementation while maintaining reliability and code quality. In recent years, Artificial Intelligence, particularly transformer-based large language models, has demonstrated strong potential in assisting programming tasks through automated code synthesis and contextual understanding of natural language instructions. This study presents the development and assessment of an AI-driven code generation platform designed to translate user-provided textual requirements into executable source code. Unlike conventional generation systems, the proposed framework incorporates contextual interpretation, automated validation using predefined test cases, and refinement mechanisms to improve readability and structural consistency of the generated output. An examination of existing AI-assisted coding systems reveals improvements in productivity; however, concerns related to adaptability across domains, interpretability of model decisions, and secure deployment remain insufficiently addressed. To explore these aspects, a prototype implementation was constructed and evaluated using widely recognized benchmark datasets, including HumanEval and CodeSearchNet. Performance assessment indicates measurable gains in functional correctness and development efficiency when compared to traditional manual coding workflows. The results support the premise that AI-enabled development tools can operate as collaborative assistants rather than replacements for programmers. Further work is required to enhance transparency, strengthen domain customization capabilities, and ensure robust security standards in practical deployments.

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