Analyzing Automatic Code Generation for Learning Models in Generative AI

Independent code generation models produced by generative AI provide a new way to software development. These models automatically generate code using machine learning based on input samples. This study examines the fundamentals, applications, problems, and future prospects of AI-related automated code generation technologies. Model-based software, domain-specific code, and testing procedures are examples of these research topics. Performance analysis and assessment are used to assess the efficacy, efficiency, and reliability of several automated code generating methods. The fact that these models have pros and cons and room for development is highlighted.