Functional and Defect Study in Deep Learning Libraries: A Complex Network Perspective

Deep learning libraries have emerged as critical software systems for a diverse range of deep learning applications. However, the stability and reliability of these libraries are increasingly challenged by their rapidly expanding scale. In this article, we utilize the function call graph as a model to represent deep learning libraries and conduct an empirical study using an innovative approach based on complex network theory. This method facilitates a thorough exploration of the topological characteristics and functionalities of deep learning libraries, revealing their scale-free and small-world properties. Leveraging the characteristics, we utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-core decomposition to pinpoint critical functions within the libraries, and conduct a comprehensive analysis to discern the characteristics of their functionalities. Furthermore, we have compiled a comprehensive dataset comprising 12 774 defective functions within these libraries. This dataset enables us to analyze and compare the distribution and trend of defects across the investigated deep learning libraries, while examining the patterns of defect propagation. Our research presents 14 significant findings, offering insights for researchers in software reliability and testing.