In the context of today's intelligent era, chips, as the core components of smart electronic devices, play a crucial role in various fields such as Artificial Intelligence, the Internet of Things, and 5G communication. Ensuring the correctness, security, and reliability of chips is of utmost importance. In the chip development process, developers first use hardware description languages to implement the chip design in software form (i.e., chip design programs), followed by physical design and finally tape-out (i.e., production and manufacturing). As the foundation of chip design and manufacturing, the quality of the chip design program directly affects the quality of the chip. Therefore, testing the chip design program is of significant research value. Early chip design program testing methods primarily relied on manually designed test cases by developers to test the chip design program, which often required substantial manual effort and time. With the increasing complexity of chip design programs, various simulation-based automated testing methods have been proposed, improving the efficiency and effectiveness of chip design program testing. In recent years, more and more researchers have been dedicated to applying intelligent methods such as machine learning, deep learning, and large language models (LLMs) to the field of chip design program testing. This paper surveys 88 academic papers related to intelligent testing of chip design programs, summarizing and categorizing the existing achievements from three perspectives: test input generation, test oracle construction, and test execution optimization. It focuses on the evolution of chip design program testing methods from the machine learning stage to the deep learning stage and then to the large language model stage, exploring the potential of different stages' methods in improving testing efficiency and coverage, as well as reducing testing costs. Additionally, it introduces research datasets and tools in the field of chip design program testing and envisions future development directions and challenges.