Code Generation Empowered by Natural Language Processing and Machine Learning Algorithms

Abstract

The goal of this study is to revolutionize code creation processes by investigating the synergistic union of machine learning (ML) and natural language processing (NLP). Enterprising non-programmers with entrance barriers, traditional approaches to code generation frequently demand expert-level programming expertise. Development teams can communicate coding tasks in natural language by utilizing NLP techniques like language modeling and semantic parsing. This helps to close the gap between human intent and instructions that can be executed by a computer. By incorporating ML techniques, the system may also more effectively understand and produce code that is compatible with a wider range of programming languages and paradigms. This research clarifies the revolutionary potential of NLP and ML-driven code creation and highlights its consequences for software development efficiency, accessibility, and innovation through an extensive assessment of current developments and case examples.