一句话：综述 NLP 辅助的软件测试方案，覆盖学术文献与工业界从自然语言需求生成测试用例的进展。
从自然语言需求出发编写测试用例依赖大量人工，过程繁琐、重复且易错。本文综述了利用自然语言处理（NLP）实现自主软件测试的研究与工业实践，重点介绍了大规模语言模型与迁移学习推动下的 NLP 辅助测试进展，并指出近年来多家公司已将“人类语言到测试用例”的转换功能嵌入产品。文章不对单一方法进行新实验评估，而是对文献与软件测试产业中的相关方案进行概览和归纳，展示该方向的整体图景与趋势。
The process of creating test cases from requirements written in natural language (NL) requires intensive human efforts and can be tedious, repetitive, and error-prone. Thus, many studies have attempted to automate that process by utilizing Natural Language Processing (NLP) approaches. Furthermore, with the advent of massive language models and transfer learning techniques, people have introduced various advancements in NLP-assisted software testing with promising results. More notably, in recent years, not only have researchers been engrossed in solving the above task, but many companies have also embedded the feature to translate from human language to test cases their products. This paper presents an overview of NLP-assisted solutions being used in both the literature and the software testing industry.