Coverage Directed Test

Coverage-directed testing (CDT) aims to efficiently generate test cases that maximize code or model coverage, improving software and hardware reliability. Current research focuses on leveraging large language models (LLMs) to automate test case generation, particularly for complex systems like large language models themselves and deep learning libraries, often employing techniques like fuzzing and prompt engineering to enhance test diversity and effectiveness. This approach significantly reduces the manual effort required for testing, leading to more robust and reliable systems across various domains, including software, hardware, and AI.