A Survey and Prospects on Unit Test Case Generation Techniques Based on Large Language Models

Automated unit test case generation is a critical research topic in the field of software engineering. Numerous scholars at home and abroad have devoted considerable efforts to addressing this problem and have achieved fruitful results. In recent years, the emergence of large language models (LLMs), such as the GPT series, LLaMA series, DeepSeek series, T5, and their variants tailored for source code, has introduced new technical pathways for unit test case generation, thereby reigniting widespread scholarly interest in this area. To further promote the theoretical and practical development of LLM-based unit test case generation, this study presents a comprehensive survey and an outlook on future research. It reviews and analyzes the evolution of unit test case generation techniques, from early exploratory efforts to the current stage driven by large models. Starting from two main categoriestechniques that rely solely on LLMs and those that integrate traditional static analysisthe current state of research and recent advancements in LLM-based test generation are discussed. On this basis, the study summarizes the major issues and challenges in the field and envisions possible future research directions. This work systematically presents the developmental trajectory, latest progress, and future prospects of this domain, offering valuable insights and guidance for subsequent research and innovation.