Efficient Dynamic Algorithms to Predict Short Races

Abstract

We introduce and study the problem of detecting short races in an observed trace. Specifically, for a race type $R$, given a trace $\sigma$ and window size $w$, the task is to determine whether there exists an $R$-race $(e_1, e_2)$ in $\sigma$ such that the subtrace starting with $e_1$ and ending with $e_2$ contains at most $w$ events. We present a monitoring framework for short-race prediction and instantiate the framework for happens-before and sync-preserving races, yielding efficient detection algorithms. Our happens-before algorithm runs in the same time as FastTrack but uses space that scales with $\log w$ as opposed to $\log |\sigma|$. For sync-preserving races, our algorithm runs faster and consumes significantly less space than SyncP. Our experiments validate the effectiveness of these short-race detection algorithms: they run more efficiently, use less memory, and detect significantly more races under the same budget, offering a reasonable balance between resource usage and predictive power.