Deep Learning Library Testing: Definition, Methods and Challenges

Abstract

Recently, software systems powered by deep learning (DL) techniques have significantly facilitated people’s lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying optimization and computation. However, like traditional software, DL libraries are not immune to bugs. These bugs may be propagated to programs and software developed based on DL libraries, thereby posing serious threats to users’ personal property and safety. Studying the characteristics of DL libraries, their associated bugs, and the corresponding testing methods is crucial for enhancing the security of DL systems and advancing the widespread application of DL technology. This paper provides an overview of the testing research on various DL libraries, discusses the strengths and weaknesses of existing methods, and provides guidance and reference for the application of DL library testing methods. This paper first introduces the workflow of DL underlying libraries and the characteristics of three kinds of DL libraries involved, namely DL framework, DL compiler, and DL hardware library. Subsequently, this paper constructs a literature collection pipeline and comprehensively summarizes existing testing methods on these DL libraries to analyze their effectiveness and limitations. It also reports findings and the challenges of existing DL library testing in real-world applications for future research.