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Predictive data race detectors find data races that exist in executions otherthan the observed execution. Smaragdakis et al. introduced thecausally-precedes (CP) relation and a polynomial-time analysis for sound (nofalse races) predictive data race detection. However, their analysis cannotscale beyond analyzing bounded windows of execution traces. This workintroduces a novel dynamic analysis called Raptor that computes CP soundly andcompletely. Raptor is inherently an online analysis that analyzes and finds allCP-races of an execution trace in its entirety. An evaluation of a prototypeimplementation of Raptor shows that it scales to program executions that theprior CP analysis cannot handle, finding data races that the prior CP analysiscannot find.

Online Set-Based Dynamic Analysis for Sound Predictive Race Detection