Online Set-Based Dynamic Analysis for Sound Predictive Race Detection
This work addresses the problem of scalable predictive race detection for software developers, though it is incremental as it builds directly on the prior CP analysis.
The paper tackles the scalability limitation of prior sound predictive race detection methods by introducing Raptor, an online dynamic analysis that computes the causally-precedes relation soundly and completely, enabling it to handle larger program executions and find more data races than the previous approach.
Predictive data race detectors find data races that exist in executions other than the observed execution. Smaragdakis et al. introduced the causally-precedes (CP) relation and a polynomial-time analysis for sound (no false races) predictive data race detection. However, their analysis cannot scale beyond analyzing bounded windows of execution traces. This work introduces a novel dynamic analysis called Raptor that computes CP soundly and completely. Raptor is inherently an online analysis that analyzes and finds all CP-races of an execution trace in its entirety. An evaluation of a prototype implementation of Raptor shows that it scales to program executions that the prior CP analysis cannot handle, finding data races that the prior CP analysis cannot find.