This thesis investigates the capabilities of two advanced Large Language Models(LLMs) OpenAI’s ChatGPT-4 and Google’s Gemini Advanced in the domain ofSoftware engineering. While LLMs are widely utilized across various applications,including text summarization and synthesis, their potential for detecting and correct-ing programming errors has not been thoroughly explored. This study aims to fill thisgap by conducting a comprehensive literature search and experimental comparisonof ChatGPT-4 and Gemini Advanced using the QuixBugs and LeetCode benchmarkdatasets, with specific focus on Python and Java programming languages. The re-search evaluates the models’ abilities to detect and correct bugs using metrics suchas Accuracy, Recall, Precision, and F1-score.Experimental results presets that ChatGPT-4 consistently outperforms GeminiAdvanced in both the detection and correction of bugs. These findings provide valu-able insights that could guide further research in the field of LLMs.