Software developers frequently adopt deep learning (DL) libraries to incorporate learning solutions into software systems. However, misuses of these libraries can cause various DL faults. Among them, tensor shape faults are most prevalent. Tensor shape faults occur when restriction conditions of operations are not met, leading to many system crashes. To support efficient detection and fixing of these faults, we conduct an empirical study to obtain a deep insight. We construct SFData, a set of 146 buggy programs with crashing tensor shape faults (i.e., those causing programs to crash). By analyzing the faults in SFData, we categorize them into four types and get some valuable observations.

中文翻译：

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深度学习系统中张量形状故障的实证研究

软件开发人员经常采用深度学习 (DL) 库将学习解决方案整合到软件系统中。但是，滥用这些库会导致各种 DL 错误。其中，张量形状故障最为普遍。当不满足操作的限制条件时会发生张量形状错误，导致许多系统崩溃。为了支持有效检测和修复这些故障，我们进行了实证研究以获得深入的洞察力。我们构建了 SFData，这是一组 146 个具有崩溃张量形状错误（即导致程序崩溃的错误）的错误程序。通过分析SFData中的故障，我们将它们分为四种类型并得到一些有价值的观察结果。