Dynamic Race Detection With O(1) Samples

TL;DR

This paper introduces a novel sub-linear time dynamic race detection algorithm that uses constant sampling to efficiently identify data races in large-scale software, significantly reducing analysis time while maintaining accuracy.

Contribution

It presents the first sub-linear (o(n)) dynamic race detection algorithm based on sampling, enabling faster analysis with high probability of detecting races without false positives.

Findings

Runs in sub-linear time, significantly faster than previous methods

Detects data races with high probability in traces far from race-free

Experimental results show low runtime and effective race detection

Abstract

Happens before-based dynamic analysis is the go-to technique for detecting data races in large scale software projects due to the absence of false positive reports. However, such analyses are expensive since they employ expensive vector clock updates at each event, rendering them usable only for in-house testing. In this paper, we present a sampling-based, randomized race detector that processes only constantly many events of the input trace even in the worst case. This is the first sub-linear time (i.e., running in o(n) time where n is the length of the trace) dynamic race detection algorithm; previous sampling based approaches like Pacer run in linear time (i.e., O(n)). Our algorithm is a property tester for HB-race detection -- it is sound in that it never reports any false positive, and on traces that are far, with respect to hamming distance, from any race-free trace, the algorithm detects an HB-race with high probability. Our experimental evaluation of the algorithm and its comparison with state-of-the-art deterministic and sampling based race detectors shows that the algorithm does indeed have significantly low running time, and detects races quite often.