To detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide soundness \emph{or} precision incur significant limitations.

This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) \emph{Precise} data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called \emph{RaceChaser} that provides low, bounded run-time overhead. (2) \emph{Sound} race detection benefits analyses and tools whose correctness relies on knowledge of \emph{all} potential data races. We present a sound, efficient approach called \emph{Caper} that combines static and dynamic analysis to catch all data races in observed runs. RaceChaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser.

Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems.