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Dynamic data race prediction aims to identify races based on a single programrun represented by a trace. The challenge is to remain efficient while being assound and as complete as possible. Efficient means a linear run-time asotherwise the method unlikely scales for real-world programs. We introduce anefficient, near complete and often sound dynamic data race prediction methodthat combines the lockset method with several improvements made in the area ofhappens-before methods. By near complete we mean that the method is complete intheory but for efficiency reasons the implementation applies some optimizationsthat may result in incompleteness. The method can be shown to be sound for twothreads but is unsound in general. We provide extensive experimental data thatshows that our method works well in practice.

Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)