Testing Storage-System Correctness: Challenges, Fuzzing Limitations, and AI-Augmented Opportunities

2026-02-02

AI Summary

Storage systems harbor subtle bugs related to persistence, consistency, and crash recovery that are difficult to expose. Traditional testing approaches are hindered by challenges such as non-deterministic thread interleavings, long-running state evolutions, and cross-layer semantic gaps. This work proposes a storage-centric testing taxonomy that systematically organizes techniques including concurrent testing, crash-consistency validation, and distributed fault injection, while uncovering a fundamental mismatch between conventional fuzzing and storage semantics. To address this gap, the paper introduces an AI-enhanced fuzzing framework that incorporates state awareness and semantic guidance. It presents the first systematic demonstration of AI’s potential to improve correctness verification in storage systems, offering a novel direction for integrating intelligent techniques into system testing methodologies.

Technology Category

• Search and Optimization: Distributed Search
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

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

Problem

• storage-system correctness
• durability
• consistency
• crash recovery
• ordering violations

Innovation

• AI-augmented testing
• storage-system correctness
• semantic-aware fuzzing
• state-aware guidance
• crash-consistency validation