一句话：提出面向同步反转数据竞争的乐观动态预测方法，以发现调度不确定性下遗漏的数据竞争。
动态数据竞争检测是保障并发软件可靠性的关键技术，但线程调度的非确定性会导致漏报。预测性竞争检测通过推断可暴露竞争的其他执行来缓解该问题，无需重跑程序。其核心问题是：给定并发程序执行的 trace σ，能否通过正确重排 σ 暴露数据竞争？现有先进技术要么难以扩展到真实并发软件的执行，要么只能暴露有限类别的竞争，例如无需反转同步操作顺序即可暴露的竞争。本文关注需要反转同步操作顺序的同步反转数据竞争，提出乐观预测方法；标题表明其目标是扩展可预测竞争的类别并兼顾可扩展性。摘要未提供具体算法细节、评估方式与实验结果。
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 inferring 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 σ of an execution of a concurrent program, can σ 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.