Test Case

Test case research focuses on optimizing the creation, selection, and prioritization of tests to improve software development efficiency and quality. Current research emphasizes leveraging machine learning, particularly large language models (LLMs) and neural networks, to automate test generation, enhance test understandability, and improve test case prioritization strategies like those based on feature selection or learning-to-rank models. These advancements aim to reduce the time and cost associated with software testing, ultimately leading to more robust and reliable software systems. Furthermore, research explores the application of these techniques across diverse domains, including deep learning compilers and even career counseling.