Taxonomy of Aging-related Bugs in Deep Learning Libraries

Deep learning libraries are the cornerstone of deep learning systems, and millions of deep learning applications are built on top of deep learning libraries. Due to long-term continuous running, many numerical operations and heavy dependence on resources, deep learning libraries are prone to the effects of software aging. Aging in deep learning libraries can threaten the reliability of deep learning systems and make training and application of deep learning more time-consuming and expensive, causing users to lose confidence in it. In this work, we manually screened 138 bug reports containing aging-related bugs from a total of 13,694 bug reports in four popular deep learning libraries (i.e., TensorFlow, MXNET, PaddlePaddle and MindSpore). We analyzed the information in these 138 bug reports to answer three questions: What categories of aging-related bugs exist in deep learning libraries? What is the distribution of different categories of aging-related bugs in deep learning libraries? Which deep learning phases are most susceptible to software aging? Finally, we conducted a fine-grained taxonomy of aging-related bugs, including four levels and seventeen categories, and obtained eight important findings with corresponding practical implications.