An Empirical Study on Bugs Inside TensorFlow

In recent years, deep learning has become a hot research topic. Although it achieves incredible positive results in some scenarios, bugs inside deep learning software can introduce disastrous consequences, especially when the software is used in safety-critical applications. To understand the bug characteristic of deep learning software, researchers have conducted several empirical studies on deep learning bugs. Although these studies present useful findings, we notice that none of them analyze the bug characteristic inside a deep learning library like TensorFlow. We argue that some fundamental questions of bugs in deep learning libraries are still open. For example, what are the symptoms and the root causes of bugs inside TensorFlow, and where are they? As the underlying library of many deep learning projects, the answers to these questions are useful and important, since its bugs can have impacts on many deep learning projects. In this paper, we conduct the first empirical study to analyze the bugs inside a typical deep learning library, i.e., TensorFlow. Based on our results, we summarize 5 findings, and present our answers to 2 research questions. For example, we find that the symptoms and root causes of TensorFlow bugs are more like ordinary projects (e.g., Mozilla) than other machine learning libraries (e.g., Lucene). As another example, we find that most TensorFlow bugs reside in its interfaces (26.24%), learning algorithms (11.79%), and how to compile (8.02%), deploy (7.55%), and install (4.72%) TensorFlow across platforms.