Data races are a real problem for parallel software, yet hard to detect.
Sound predictive analysis observes a program execution and detects data races
that exist in some other, unobserved execution.
However, existing predictive analyses miss races because they do not
scale to full program executions or do not precisely incorporate data and control dependence.

This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions.
An evaluation using real, large Java programs shows that these approaches
detect more data races than the closest related approaches,
thus advancing the state of the art in sound predictive race detection.