The field of software defect prediction and localization is moving towards leveraging pre-trained language models and large language models to improve accuracy and robustness. Recent studies have shown that these models can achieve superior results in defect prediction and localization, especially in evolving software environments. However, challenges such as concept drift, class imbalance, and verification latency still need to be addressed. Furthermore, the use of large language models in defect localization has shown promise, but also highlights the need for further refinement in their reasoning and computational efficiency. Noteworthy papers in this area include CodeFlowLM, which introduces an incremental learning framework for Just-In-Time Software Defect Prediction, and Beyond Code Pairs, which presents an automated dataset generation pipeline for improving code translation in low-resource programming domains. Exploring the Potential and Limitations of Large Language Models for Novice Program Fault Localization is also notable for its evaluation of large language models in fault localization, and MANTRA is a framework for multi-stage adaptive noise treatment during training that improves the performance of large language models in software engineering tasks.