Analysis of Natural language processing for code generation by using COPRAS Method

Natural Language Processing has emerged as a powerful tool in various fields, including code generation. This paper explores the application of NLP techniques for the automatic generation of code from natural language specifications. The goal is to bridge the gap between human-readable requirements and machine-executable code, making software development more accessible and efficient. NLP for code generation serves as a bridge between human-readable natural language specifications and machine-executable code. This is particularly significant as it facilitates communication between developers and non-technical stakeholders, allowing them to contribute to the software development process without detailed programming knowledge. As the demand for software development continues to grow, there is a persistent talent gap in the industry. NLP for code generation can contribute to addressing this gap by allowing individuals with domain expertise to participate in software development without requiring extensive programming skills. The COPRAS-G method requires identifying selection criteria; evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. From the result TRANX is got the first rank whereas is the Tree2 tree is having the lowest rank.