Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products. As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent, making it an area ripe for innovative approaches such as the use of LLMs. This paper provides a comprehensive review of the utilization of LLMs in software testing. It analyzes 102 relevant studies that have used LLMs for software testing, from both the software testing and LLMs perspectives. The paper presents a detailed discussion of the software testing tasks for which LLMs are commonly used, among which test case preparation and program repair are the most representative. It also analyzes the commonly used LLMs, the types of prompt engineering that are employed, as well as the accompanied techniques with these LLMs. It also summarizes the key challenges and potential opportunities in this direction. This work can serve as a roadmap for future research in this area, highlighting potential avenues for exploration, and identifying gaps in our current understanding of the use of LLMs in software testing.

中文翻译：

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使用大型语言模型进行软件测试：调查、景观和愿景

预训练的大型语言模型（LLM）最近已成为自然语言处理和人工智能领域的突破性技术，能够处理大规模数据集并在各种任务中表现出卓越的性能。同时，软件测试是一项至关重要的工作，是保证软件产品质量和可靠性的基石。随着软件系统的范围和复杂性不断增长，对更有效的软件测试技术的需求变得越来越迫切，这使其成为使用法学硕士等创新方法的成熟领域。本文对法学硕士在软件测试中的运用进行了全面的回顾。它从软件测试和法学硕士的角度分析了 102 项使用法学硕士进行软件测试的相关研究。本文详细讨论了法学硕士常用的软件测试任务，其中测试用例准备和程序修复最具代表性。它还分析了常用的法学硕士、所采用的即时工程类型以及这些法学硕士的附带技术。它还总结了这个方向的主要挑战和潜在机遇。这项工作可以作为该领域未来研究的路线图，突出潜在的探索途径，并找出我们目前对法学硕士在软件测试中的使用的理解中的差距。