In recent years, software systems powered by deep learning (DL) techniques
have significantly facilitated people's lives in many aspects. As the backbone
of these DL systems, various DL libraries undertake the underlying optimization
and computation. However, like traditional software, DL libraries are not
immune to bugs, which can pose serious threats to users' personal property and
safety. Studying the characteristics of DL libraries, their associated bugs,
and the corresponding testing methods is crucial for enhancing the security of
DL systems and advancing the widespread application of DL technology. This
paper provides an overview of the testing research related to various DL
libraries, discusses the strengths and weaknesses of existing methods, and
provides guidance and reference for the application of the DL library. This
paper first introduces the workflow of DL underlying libraries and the
characteristics of three kinds of DL libraries involved, namely DL framework,
DL compiler, and DL hardware library. It then provides definitions for DL
underlying library bugs and testing. Additionally, this paper summarizes the
existing testing methods and tools tailored to these DL libraries separately
and analyzes their effectiveness and limitations. It also discusses the
existing challenges of DL library testing and outlines potential directions for
future research.

深度学习技术在各个领域极大地方便了人们的生活，但深度学习库作为这些系统的支柱，同样会受到漏洞的威胁。本文提供了与深度学习库相关的测试研究概述，讨论了现有方法的优势、劣势，并为深度学习库的应用提供指导和参考，同时还总结了针对这些库的现有测试方法和工具，分析了其有效性和局限性，并探讨了深度学习库测试的挑战和未来的研究方向。

深度学习库测试方法概述

A Survey of Deep Learning Library Testing Methods

The growing use of deep neural networks in safety-critical applications makes
it necessary to carry out adequate testing to detect and correct any incorrect
behavior for corner case inputs before they can be actually used. Deep neural
networks lack an explicit control-flow structure, making it impossible to apply
to them traditional software testing criteria such as code coverage. In this
paper, we examine existing testing methods for deep neural networks, the
opportunities for improvement and the need for a fast, scalable, generalizable
end-to-end testing method. We also propose a coverage criterion for deep neural
networks that tries to capture all possible parts of the deep neural network's
logic.

对神经网络的测试方法进行研究，提出了一种覆盖所有逻辑的覆盖率准则，并探讨了改进方法和需要快速、可扩展、通用的端到端测试方法的必要性。