Dynamic data race detection has emerged as a key technique for ensuring reliability of concurrent software in practice. However, dynamic approaches can often miss data races owing to non-determinism in the thread scheduler. Predictive race detection techniques cater to this shortcoming by trying to infer alternate executions that may expose data races without re-executing the underlying program. More formally, the dynamic data race prediction problem asks, given a trace $\sigma$ of an execution of a concurrent program, can $\sigma$ be correctly reordered to expose a data race? Existing state-of-the art techniques for data race prediction either do not scale to executions arising from real world concurrent software, or only expose a limited class of data races, such as those that can be exposed without reversing the order of synchronization operations.

In general, exposing data races by reasoning about synchronization reversals is an intractable problem. In this work, we identify a class of data races, called Optimistic Sync(hronization)-Reversal races that can be detected in a tractable manner and often include non-trivial data races that cannot be exposed by prior tractable techniques. We also propose a sound algorithm $\mathsf{OSR}$ for detecting all optimistic sync-reversal data races in overall quadratic time, and show that the algorithm is optimal by establishing a matching lower bound. Our experiments demonstrate the effectiveness of $\mathsf{OSR}$ — on our extensive suite of benchmarks, $\mathsf{OSR}$ reports the largest number of data races, and is scales well to large execution traces.

Note: Proofs of theorems and lemmas, and some detailed evaluation report are deferred to appendix (supplementary material). Further, link to anonymous source code of our tool is provided in the field “Information about Available Data / Artifacts”.