A Comparative Analysis Of Chatgpt Vs Gemini As Code Generation Tools

Abstract

This study compares two AI models, ChatGpt and Gemini, to evaluate their performance in the field of software development. The research collects information from previous research and evaluates their performance based on parameters such as the number of lines, execution time, prompt consistency, error handling, and performance across different difficult levels. The study focus is to provide results that help developers and researchers choose the platform that fulfills their requirements. Moreover, it also contributes to the ongoing discussion. Furthermore, the research reveals differences between the models by illustrating ChatGPT capabilities in generating desired code formats and Gemini AI's effectiveness in analyzing errors. The study evaluates their performance and error detection capabilities across various difficulty levels, including high, medium, and low. Additionally, the research noted the execution time of generated code with the help of Java code. After that, for performance evaluation, the research includes observation from solving 20 LeetCode questions, and for assessing error handling, it uses code samples with predefined errors for a more focused analysis. Regardless of differences in complexity and performance across various tasks, the results highlighted the usage of both models in particular cases. Eventually, this research provides a clear understanding of AI models by illustrating results in a meaningful way, which allows researchers to reasoned decisions according to their requirements and effectively use the AI

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