Software testing is a crucial phase in the software development lifecycle, essential for delivering secure and reliable software systems. Detection of software bugs is critical in order to deploy robust systems to end users, avoiding unexpected behaviors and maintaining high software quality. However, traditional testing methods frequently rely on manual effort or static rule-based tools, approaches that can be time-consuming and resource-intensive. With the advancement of Artificial Intelligence (AI), Large Language Models (LLMs) have emerged as a breakthrough, demonstrating impressive capabilities in various scenarios, such as machine translation, text summarization and natural language understanding. Motivated by these promising results, researchers have begun exploring the potential of LLMs for a wide range of software engineering tasks, including applications within software testing.