Deep Learning Library Testing: Definition, Methods and Challenges
School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学网络空间安全学院）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）
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.
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；University of Waterloo（滑铁卢大学）；Huazhong University of Science and Technology（华中科技大学）
针对现有 DL 模型生成技术层 API 覆盖不足的问题，本文提出 COMET，利用变异算子、覆盖引导搜索与模型综合提升测试多样性，并在八个 DL 库中发现 32 个新缺陷。
QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing
Manufacturing Industry Chain Collaboration Industrial Software Key Laboratory of Sichuan Province（四川省制造业产业链协同工业软件重点实验室）；Southwest Jiaotong University（西南交通大学）；State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang University（浙江大学）；Kyushu University（九州大学）；University of Luxembourg（卢森堡大学）；Institute of AI for Industries（产业人工智能研究院）
