Efficient data race prediction with incremental reasoning on time-stamped lock history

Abstract

We present an efficient data race prediction algorithm that uses lock-reordering based incremental search on time-stamped lock histories for solving multiple races effectively. We balance prediction accuracy, coverage, and performance with a specially designed pairwise reachability algorithm that can store and re-use past search results, thereby, amortizing the cost of reasoning over redundant and overlapping search space. Compared to graph-based search algorithms, our algorithm incurs much smaller overhead due to amortization, and can potentially be used while a program under test is executing. To demonstrate such a possibility, we implemented our approach as an incremental Predictive Analysis (iPA) module in a predictive testing framework. Our approach can handle traces with a few hundreds to half a million events, and predict known/unknown real data races with a performance penalty of less than 4% in addition to what is incurred by runtime race detectors.