The rapid growth of Large Language Models (LLMs) and AI-driven applications has propelled Vector Database Management Systems (VDBMSs) into the spotlight as a critical infrastructure component. VDBMS specializes in storing, indexing, and querying dense vector embeddings, enabling advanced LLM capabilities such as retrieval-augmented generation, long-term memory, and caching mechanisms. However, the explosive adoption of VDBMS has outpaced the development of rigorous software testing methodologies tailored for these emerging systems. Unlike traditional databases optimized for structured data, VDBMS face unique testing challenges stemming from the high-dimensional nature of vector data, the fuzzy semantics in vector search, and the need to support dynamic data scaling and hybrid query processing. In this paper, we begin by conducting an empirical study of VDBMS defects and identify key challenges in test input generation, oracle definition, and test evaluation. Drawing from these insights, we propose the first comprehensive research roadmap for developing effective testing methodologies tailored to VDBMS. By addressing these challenges, the software testing community can contribute to the development of more reliable and trustworthy VDBMS, enabling the full potential of LLMs and data-intensive AI applications.

翻译：大型语言模型（LLMs）与人工智能驱动应用的快速增长，已将向量数据库管理系统（VDBMSs）推至聚光灯下，使其成为关键的基础设施组件。VDBMS专门用于存储、索引和查询稠密向量嵌入，从而支持诸如检索增强生成、长期记忆与缓存机制等先进的LLM能力。然而，VDBMS的爆炸式采用速度已超过了为其量身定制的严格软件测试方法的发展步伐。与针对结构化数据优化的传统数据库不同，VDBMS面临着源于向量数据高维特性、向量搜索中的模糊语义，以及需要支持动态数据扩展与混合查询处理等独特测试挑战。本文首先对VDBMS缺陷进行实证研究，并识别出测试输入生成、预言定义和测试评估中的关键挑战。基于这些洞见，我们提出了首个全面的研究路线图，旨在开发针对VDBMS的有效测试方法。通过应对这些挑战，软件测试社区可以为开发更可靠、更可信的VDBMS做出贡献，从而充分释放LLMs与数据密集型人工智能应用的潜力。