This study explores how Large Language Models (LLMs) are transforming software development by enhancing bug classification and code analysis. We compare advanced models such as Generative Pre-trained Transformer 3.5 Turbo (GPT‑3.5 Turbo) and Generative Pre-trained Transformer 4 omni (GPT‑4o) with traditional approaches like Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), and FastText. While conventional models demonstrated reasonable performance with an accuracy of approximately 0.77 and an Area Under the Receiver Operating Characteristic Curve (AUC) score close to 0.85, Generative Pre-trained Transformer 4 omni (GPT‑4o) surpassed them by achieving an accuracy of 0.8225 and an AUC score of 0.8817, highlighting its superior ability to interpret complex code semantics. Although FastText was computationally efficient, it lagged behind in performance. The use of Google Cloud’s A100 Graphics Processing Units (GPUs) facilitated efficient training, and the Chat Generative Pre-trained Transformer Application Programming Interface (ChatGPT API) enabled real-time classification with minimal infrastructure requirements. Despite their benefits, Large Language Models (LLMs) require significantly more computational resources and present challenges related to explainability. Our findings indicate that while LLMs can significantly improve software defect detection, effective deployment necessitates a careful balance between performance, resource consumption, and model interpretability.