一句话：通过混合方法调研实践者对自动化测试生成的期望，并与近三年研究对比，指出研究与实践的差距及未来方向。
自动化测试生成旨在帮助开发者编写高质量测试并减少手工编写测试代码的负担。尽管该领域已有近五十年研究，但实践者究竟期望自动化测试生成工具具备什么能力、现有研究是否满足这些需求仍不清晰。本文采用混合方法展开实证研究：首先对13位专业人士进行半结构化访谈并作定性分析，随后对来自46个国家、五大洲的339位实践者进行定量问卷调查；最后系统回顾2022至2024年顶级会议论文，将当前研究成果与实践者期望进行对比。通过这一对比，作者识别出自动化测试生成研究与实践需求之间的差距，并为研究者提出未来研究方向，以弥合二者之间的鸿沟。
Automated test generation can help developers craft high-quality software tests while mitigating the manual effort needed for writing test code. Despite significant research efforts in automated test generation for nearly 50 years, there is a lack of clarity about what practitioners expect from automated test generation tools and whether the existing research meets their needs. To address this issue, we follow a mixed-methods approach to gain insights into practitioners' expectations of automated test generation. We first conduct the qualitative analysis from semi-structured interviews with 13 professionals, followed by a quantitative survey of 339 practitioners from 46 countries across five continents. We then conduct a literature review of premier venue papers from 2022 to 2024 (in the last three years) and compare current research findings with practitioners' expectations. From this comparison, we outline future research directions for researchers to bridge the gap between automated test generation research and practitioners' expectations.