Evaluating AI Efficiency in Backend Software Development - A Comparative Analysis Across Frameworks

Abstract

Artificial intelligence (AI) has increasingly been adopted to assist backend software development through code generation, automation, and prompt optimization. The objective of this study was to compare the performance of various AI code generation models—namely ChatGPT, DeepSeek, Gemini, and GitHub Copilot—across multiple backend frameworks, including Node.js, Flask, .NET, and Laravel. A standardized Secure File Transfer Protocol (SFTP) application was implemented to evaluate AI-generated code using software engineering benchmarks. These benchmarks included code correctness, runtime efficiency, maintainability, readability, and error rate. In addition, the study applied three prompt optimization strategies—self-refinement loops, few-shot injection, and role-based contextualization—to determine their effect on code quality and reliability. The implementation involved prompt testing under controlled conditions and the use of a custom benchmarking script to quantify performance. Results showed that AI performance varied significantly based on both the underlying framework and prompt strategy. Node.js exhibited the highest compatibility with AI-generated code, while .NET demonstrated superior correctness and maintainability. Prompt optimization strategies were shown to consistently improve output quality, with self-refinement loops providing the most reliable improvements. It was concluded that AI tools can significantly accelerate backend development when guided by structured prompts and evaluated under standardized conditions. These findings support the integration of AI-assisted code generation into backend engineering practices to enhance productivity and reduce technical debt.