The field of software testing is experiencing a significant shift with the integration of large language models (LLMs). Recent developments have shown that LLMs can be leveraged to improve the efficiency and effectiveness of testing, particularly in the areas of fuzz testing and test case generation. The use of LLMs has been shown to enhance test validity, API coverage, and bug detection performance, outperforming traditional testing approaches. Furthermore, the application of LLMs in emerging programming languages and GraphQL APIs has demonstrated great promise in identifying complex vulnerabilities and improving overall software reliability. Noteworthy papers in this area include: LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models, which proposes an adaptive LLM-based fuzzing framework for emerging programming languages. PrediQL: Automated Testing of GraphQL APIs with LLMs, which presents a retrieval-augmented, LLM-guided fuzzer for GraphQL APIs. ATGen: Adversarial Reinforcement Learning for Test Case Generation, which introduces a framework that trains a test case generator via adversarial reinforcement learning. The Pursuit of Diversity: Multi-Objective Testing of Deep Reinforcement Learning Agents, which introduces a multi-objective search approach for discovering diverse failure scenarios in deep reinforcement learning agents.