SmartTrack: Efficient Predictive Race Detection
This work addresses performance bottlenecks in predictive race detection for software testing, offering an incremental improvement over prior methods.
The paper tackles the problem of data race detection by introducing SmartTrack, an algorithm that optimizes predictive analyses to detect more races than FastTrack while achieving competitive performance, with results showing it matches FastTrack's speed.
Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.