  Automatic detection of software bugs is a critical task in software security.
Many static tools that can help detect bugs have been proposed. While these
static bug detectors are mainly evaluated on general software projects call
into question their practical effectiveness and usefulness for machine learning
libraries. In this paper, we address this question by analyzing five popular
and widely used static bug detectors, i.e., Flawfinder, RATS, Cppcheck,
Facebook Infer, and Clang static analyzer on a curated dataset of software bugs
gathered from four popular machine learning libraries including Mlpack, MXNet,
PyTorch, and TensorFlow with a total of 410 known bugs. Our research provides a
categorization of these tools' capabilities to better understand the strengths
and weaknesses of the tools for detecting software bugs in machine learning
libraries. Overall, our study shows that static bug detectors find a negligible
amount of all bugs accounting for 6/410 bugs (0.01%), Flawfinder and RATS are
the most effective static checker for finding software bugs in machine learning
libraries. Based on our observations, we further identify and discuss
opportunities to make the tools more effective and practical.
