| 摘要: |
| 随着大数据和计算能力的快速发展, 深度学习技术取得巨大突破, 并迅速成为一个具有众多实际应用场景和活跃研究课题的领域. 为了满足日益增长的深度学习任务开发需求, 深度学习框架应运而生. 深度学习框架作为连接应用场景和硬件平台的中间部件, 向上支撑深度学习应用的开发, 帮助用户快速构造不同的深度神经网络模型, 向下深度适配各类计算硬件, 满足不同算力架构和环境下的计算需求. 作为人工智能领域的关键基础软件, 深度学习框架中一旦存在问题, 即使是一个只有几行代码的缺陷都可能导致在其基础上构造的模型发生大规模失效, 严重威胁深度学习系统安全. 作为以深度学习框架测试为主题的研究性综述, 首先对深度学习框架发展历程和基本架构进行介绍; 其次, 通过对55篇与深度学习框架测试研究直接相关的学术论文进行梳理, 对深度学习框架缺陷特性、测试关键技术和基于不同测试输入形式的测试方法这3个方面进行系统分析和总结; 针对不同测试输入形式的特点, 重点探究如何结合测试关键技术来解决研究问题; 最后对深度学习框架测试尚未解决的难点问题进行总结以及对未来值得探索的研究方向进行展望. 可以为深度学习框架测试研究领域的相关人员提供参考和帮助, 推动深度学习框架的不断发展成熟. |
| 关键词: 深度学习框架 测试 缺陷 实证研究 |
| DOI：10.13328/j.cnki.jos.007059 |
| 分类号: |
| 基金项目:国家自然科学基金(61772055, 61872169); 中央高校基本科研业务费专项资金(2023RC06) |
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| Survey on Testing of Deep Learning Frameworks |
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MA Xiang-Yue1, DU Xiao-Ting2, CAI Qing1, ZHENG Yang3, HU Zheng3, ZHENG Zheng1
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1.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2.School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China;3.Trustworthiness Theory, Technology & Engineering Lab, Huawei Technologies Co. Ltd., Shenzhen 518129, China
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| Abstract: |
| As big data and computing power rapidly develop, deep learning has made significant breakthroughs and rapidly become a field with numerous practical application scenarios and active research topics. In response to the growing demand for the development of deep learning tasks, deep learning frameworks have arisen. Acting as an intermediate component between application scenarios and hardware platforms, deep learning frameworks facilitate the development of deep learning applications, enabling users to efficiently construct diverse deep neural network (DNN) models, and deeply adapt to various computing hardware, meeting the computational needs across different computing architectures and environments. Any issues that arise within deep learning frameworks, which serve as the fundamental software in the realm of artificial intelligence, can have severe consequences. Even a single bug in the code can trigger widespread failures within models built upon the framework, thereby posing a serious threat to the safety of deep learning systems. As a review exclusively focuses on the testing of deep learning frameworks, this study initially introduces the developmental history and basic architectures of deep learning frameworks. Subsequently, by systematically examining 55 academic papers directly related to the testing of deep learning frameworks, the study systematically analyzes and summarizes bug characteristics, key technologies for testing, and methods based on various input forms for testing. The study explores how to combine key technologies to address research problems. Lastly, it summarizes the unresolved difficulties in the testing of deep learning frameworks and provides insights into promising research directions for the future. This study can offer valuable references and guidance to individuals involved in the research field of deep learning framework testing, ultimately promoting the sustained development and maturity of deep learning frameworks. |
| Key words: deep learning (DL) framework testing bug empirical study |
