With the advancement of informationalization, the development of a variety of applications and iterative functions inevitably leads to software defects, posing significant threats to program reliability and security. Therefore, detecting and repairing software defects becomes essential yet onerous for developers in maintaining software quality. Accordingly, software engineering researchers have proposed numerous technologies over the past decades to help developers address defect-related issues. However, these technologies face serious challenges and make little progress in industrial implementation. Large language model (LLM), such as the code-based model CodeX and the prestigious ChatGPT, trained on massive datasets, can capture complex patterns and structures in code, process extensive contextual information, and flexibly adapt to various tasks. Their superior performance has attracted considerable attention from researchers. In many software engineering tasks, technologies based on LLM show significant advantages in addressing key challenges previously faced in different domains. Consequently, this study attempts to analyze and explore three defect detection domains where technologies based on LLM have been widely adopted: deep-learning library defect detection, GUI automated testing, and automated test case generation, along with one mature software defect repair domain: automated program repair (APR). This study delves into the progress of these domains and provides an in-depth discussion of their characteristics and challenges. Lastly, based on an analysis of existing research, this study summarizes the key challenges faced by these domains and technologies and offers insights for future research.