Testing Storage-System Correctness: Challenges, Fuzzing Limitations, and AI-Augmented Opportunities
Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS)（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）
Storage systems are fundamental to modern computing infrastructures, yet ensuring their correctness remains challenging in practice. Despite decades of research on system testing, many storage-system failures (including durability, ordering, recovery, and consistency violations) remain difficult to expose systematically. This difficulty stems not primarily from insufficient testing tooling, but from intrinsic properties of storage-system execution, including nondeterministic interleavings, long-horizon state evolution, and correctness semantics that span multiple layers and execution phases.
This survey adopts a storage-centric view of system testing and organizes existing techniques according to the execution properties and failure mechanisms they target. We review a broad spectrum of approaches, ranging from concurrency testing and long-running workloads to crash-consistency analysis, hardware-level semantic validation, and distributed fault injection, and analyze their fundamental strengths and limitations. Within this framework, we examine fuzzing as an automated testing paradigm, highlighting systematic mismatches between conventional fuzzing assumptions and storage-system semantics, and discuss how recent artificial intelligence advances may complement fuzzing through state-aware and semantic guidance. Overall, this survey provides a unified perspective on storage-system correctness testing and outlines key challenges
LLM-Based Test Generation: Information Sources, Generation Strategies, and Quality Evidence
Chengdu Institute of Computer Applications, Chinese Academy of Sciences（中国科学院成都计算机应用研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University（东北大学先进材料多学科研究所）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术研究院人工智能研究所）
Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
Institute of Software Chinese Academy of Sciences Beijing, China；College of Intelligence；Computing Tianjin University Tianjin, China；Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai, China
