Compatibility Issues in Deep Learning Systems: Problems and Opportunities

Deep learning (DL) systems are complex component-based systems, which consist of core program (code implementation and data), Python (language and interpreter), third-party libraries, lowlevel libraries, development tools, OS, and hardware environments. Incompatible interaction between components would cause serious compatibility issues, substantially affecting the development and deployment processes. What types of compatibility issues are frequently exposed in DL systems? What are the root causes of such issues and how do developers fix them? How far are we from automatically detecting and fixing DL compatibility issues? Although there are many existing studies on DL bugs, the characteristics of DL compatibility issues have rarely been systematically studied and the above questions remain largely unexplored. To fill this gap, we conduct the first comprehensive empirical study to characterize compatibility issues in DL systems. Through analyzing 352 DL compatibility issues classified from 3,072 posts on Stack Overflow, we present their types, manifestation stages, and symptoms. We further summarize the root causes and common fixing strategies, and conduct a tool survey on the current research status of automated detection and repair of DL compatibility issues. Our study allows researchers and practitioners to gain a better understanding of DL compatibility issues and can facilitate future tool development.

• Software and its engineering → Software defect analysis; Software libraries and repositories; • General and reference → Empirical studies.