Detecting Data Race in Network Applications using Static Analysis

Abstract

With the development of the network, there are more and more multi-thread programs. Multi-thread applications have complex operating states and suffer from data race problem. However, the existing data race detection algorithms are mostly based on dynamic methods, which cause high memory overheads. In this paper, we present DR-Frame, a static data race detector that costs lower memory and time. DR-Frame first compiles the program into an intermediate code file. Then DR-Frame simulates the code file to obtain the read and write information of the program. Finally, DR-Frame checks the data observed and detects data race of the original program. We have validated DR-Frame using three real-world network applications and the PARSEC benchmark suite. The experimental results show that DR-Frame can detect new race cases with low memory overheads, and the time cost is less than executing the program.