This study aims to evaluate the code quality produced by ChatGPT and Google Bard, identify potential risks, and propose specific recommendations.

To achieve this, two LLM evaluation datasets, comprising 246 prompts and 576 methods for code generation, were utilized to challenge the chatbots with simple and intricate Python programming tasks. Additionally, software quality metrics focused on functional correctness, maintainability, and performance efficiency were defined for comprehensive assessment.

The results indicate a low performance in both models, particularly in intricate and interconnected code scenarios. ChatGPT and Bard displayed suboptimal performance, with 30% and 17% pass rates, respectively. Additionally, ChatGPT demonstrated a pass rate of 68.90% when tasked with generating more straightforward and standalone Python methods, while Bard achieved 54.878%. Moreover, Both models showed a high compilation rate, ensuring correct syntax and executable code. However, the generated code frequently failed to deliver the intended functionality.