Paper Title
EVALUATING THE EFFECTIVENESS OF NLP-BASED AUTOMATED CODE GENERATION TOOLS FOR ENHANCED SOFTWARE DEVELOPMENT PRODUCTIVITY

Abstract
The proposed research will therefore seek to assess the level of success realized through the use of NLP affiliated automated code generation tools in software development productivity improvement. Using superior NLP models, these tools allow developers to make written descriptions as part of code useful for expediting the coding process and cutting development time. The materials and methods section mainly concentrates on comparing and evaluating several code generation tools based on NLP such as the Codex and GPT models. Based upon various case studies and controlled experimentation, these were measured up on criteria such as code readability, precision, computational speed, and compatibility of the developed tools with various development environments. Findings show that developers experience greater efficiency gains, which range between 20-40 percent, apparently on routine or environments typical code writing. Nevertheless, shallowness of the code accuracy and the ability to metabolose the context were reported, most particularly in the scenario where complicated code standards occur. Consequently, the study concludes that, though code generation tools based on NLP have much potential for increasing efficiency, more development is required for the more complex and specialized tasks. Keywords - NLP-based code generation, software development productivity, automated code generation, transformer models, Codex, GPT