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Long Zheng, Yang Li, Jie Xin, Hai-Feng Liu, Ran Zheng, Xiao-Fei Liao, Hai Jin. Minimal Context-Switching Data Race Detection with Dataflow Tracking[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-023-1569-7
Citation: Long Zheng, Yang Li, Jie Xin, Hai-Feng Liu, Ran Zheng, Xiao-Fei Liao, Hai Jin. Minimal Context-Switching Data Race Detection with Dataflow Tracking[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-023-1569-7

Minimal Context-Switching Data Race Detection with Dataflow Tracking

  • Data race is one of the most important concurrent anomalies in multi-threaded programs. Emerging constraint- based techniques are leveraged into the race detection, which is able to nd all the races that can be found by any other sound race detector. However, this constraint-based approach has serious limitations on helping programmers analyze and understand data races. First, it may report a large number of false positives due to the unrecognized dataflow propagation of the program. Second, it recommends a wide range of thread context switches to schedule the reported race (including the false one) whenever this race is exposed during the constraint solving process. This ad hoc recommendation imposes too many context switches, which complicates the data race analysis. To address those two limitations in the state-of-the-art constraint-based race detection, this paper proposes DFTracker, an improved constraint-based race detector to recommend each data race with the minimal thread context switches. Speci fically, we reduce the false positives by analyzing and tracking the dataflow in the program. By this means, DFTracker thus reduces the unnecessary analysis of false race schedules. we further propose a novel algorithm to recommend the effective race schedule with minimal thread context switches for each data race. Our experimental results on the real applications demonstrate that 1) without removing any true data races, DFTracker effectively prunes false positives by 68% in comparison with the state-of-the-art constraint-based race detector; 2) DFTracker recommends as low as 2.6~8.3 (4.7 on average) thread context switches per data race in the real world, which is 81.6% fewer context switches per data race than the state-of-the-art constraint-based race detector. Therefore, DFTracker can be used as an effective tool to understand the data race for the programmers.
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