Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However, like any other programs, they are prone to bugs. This paper deals with the subcategory of bugs named silent bugs: they lead to wrong behavior but they do not cause system crashes or hangs, nor show an error message to the user. Such bugs are even more dangerous in DL applications and frameworks due to the "black-box" and stochastic nature of the systems (the end user can not understand how the model makes decisions). This paper presents the first empirical study of Keras and TensorFlow silent bugs, and their impact on users' programs. We extracted closed issues related to Keras from the TensorFlow GitHub repository. Out of the 1,168 issues that we gathered, 77 were reproducible silent bugs affecting users' programs. We categorized the bugs based on the effects on the users' programs and the components where the issues occurred, using information from the issue reports. We then derived a threat level for each of the issues, based on the impact they had on the users' programs. To assess the relevance of identified categories and the impact scale, we conducted an online survey with 103 DL developers. The participants generally agreed with the significant impact of silent bugs in DL libraries and acknowledged our findings (i.e., categories of silent bugs and the proposed impact scale). Finally, leveraging our analysis, we provide a set of guidelines to facilitate safeguarding against such bugs in DL frameworks.

翻译：深学习( DL) 框架现在被广泛使用, 简化了复杂模型的创建, 并将其整合到各种应用程序中, 甚至是非 DL 专家。 但是, 和任何其他程序一样, 它们容易出现错误。 本文涉及名为静听错误的错误子类别: 它们会导致错误的行为, 但是它们不会造成系统崩溃或挂挂挂, 也不会向用户显示错误信息。 由于“ 黑盒子” 和系统结构的随机性, 这些错误在 DL 应用程序和框架中更加危险( 终端用户无法理解模型是如何做出决定的 ) 。 本文展示了对 Keras 和 TensorFlow 窃听器的第一次经验性研究, 以及它们对用户程序的影响。 我们从 TensorFlow GitHub 库中提取了与 Keras 有关的封闭问题类别 。 在我们收集的1, 168 问题中, 77 是可复制的静听的沉默错误 影响到用户程序。 我们根据对用户程序的影响和问题的组件进行了分类, 我们随后根据问题报告提供的信息, 得出了对每类 D 的 RB 和 D 分析 的大小 进行了威胁等级 的大小 。