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

arXiv: https://arxiv.org/abs/2602.02614 · HTML: https://mlsanity.com/paper/2602.02614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 97 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [Learning Concise Models from Long Execution Traces](https://mlsanity.com/paper/2001.05230/) (2020)
- [DeepRace: Finding Data Race Bugs via Deep Learning](https://mlsanity.com/paper/1907.07110/) (2019)
- [On Scalable Integrity Checking for Secure Cloud Disks](https://mlsanity.com/paper/2405.03830/) (2024)
- [Finding and Analyzing Crash-Consistency Bugs in Persistent-Memory File Systems](https://mlsanity.com/paper/2204.06066/) (2022)
- [The Security War in File Systems: An Empirical Study from A Vulnerability-Centric Perspective](https://mlsanity.com/paper/2204.12590/) (2022)
- [TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs](https://mlsanity.com/paper/2601.02632/) (2026)

## Abstract (arXiv)

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