论文标题
高效，接近完整且经常是合理的混合动态数据竞赛预测（扩展版本）
Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)
论文作者
论文摘要
动态数据竞赛预测旨在根据以痕迹表示的单个程序运行来识别种族。面临的挑战是在尽可能完整的同时保持高效。有效的是将线性运行时间作为现实世界程序不太可能刻度的线性运行时间。我们介绍了一种有效的，接近完整的，经常声音的动态数据竞赛预测方法，该方法将锁定方法与在发生之前发生的领域进行了一些改进。几乎完整地，我们的意思是从理论上讲，该方法是完整的，但出于效率原因，实施应用了一些可能导致不完整的优化。该方法可以证明是两个线程的声音，但通常不合理。我们提供了广泛的实验数据，表明我们的方法在实践中效果很好。
Dynamic data race prediction aims to identify races based on a single program run represented by a trace. The challenge is to remain efficient while being as sound and as complete as possible. Efficient means a linear run-time as otherwise the method unlikely scales for real-world programs. We introduce an efficient, near complete and often sound dynamic data race prediction method that combines the lockset method with several improvements made in the area of happens-before methods. By near complete we mean that the method is complete in theory but for efficiency reasons the implementation applies some optimizations that may result in incompleteness. The method can be shown to be sound for two threads but is unsound in general. We provide extensive experimental data that shows that our method works well in practice.
