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SRD: Static Data Race Detection for Concurrent Programs

Volume 14, Number 11, November 2018, pp. 2683-2691
DOI: 10.23940/ijpe.18.11.p14.26832691

Yang Zhang, Yanan Liang, and Dongwen Zhang

School of Information Science and Technology, Hebei University of Science and Technology, Shijiazhuang, 050000, China

(Submitted on August 10, 2018; Revised on September 12, 2018; Accepted on October 11, 2018)

Abstract:

Data race probably occurs when many threads concurrently access the same memory location and at least one is a write thread. Data race detection suffers from false negatives and false positives. How to detect data race and avoid false negatives and false positives has become a hot topic. This paper proposes a static data race detection methodology to eliminate false negatives and false positives. We use Soot to conduct intra-thread and inter-thread analysis. Our data race detection focuses on variable access events that are collected from call graphs. Several program analysis technologies, such as alias variable analysis, alias lock analysis, happens-before analysis, constraint graph, and slicing analysis, are used to improve the coverage and precision of the detection results. In the experimentation, several benchmarks, such as raytracer, sor, and mergesort, have been selected to evaluate our methodology. Experimental results show that SRD can not only eliminate fake races but also identify potential races. Furthermore, SRD can detect more positive races than the existing tool RVPredict.

 

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