Year: 2019
Type: preprint
Abstract: Predictive data race detectors find data races that exist in executions other than the observed execution. Smaragdakis et al. introduced the causally-precedes (CP) relation and a polynomial-time analysis for sound (no false races) predictive data race detection. However, their analysis cannot scale beyond analyzing bounded windows of execution traces. This work introduces a novel dynamic analysis called Raptor that computes CP soundly and completely. Raptor is inherently an online analysis that analyzes and finds all CP-races of an execution trace in its entirety. An evaluation of a prototype implementation of Raptor shows that it scales to program executions that the prior CP analysis cannot handle, finding data races that the prior CP analysis cannot find.
Source: arXiv (Cornell University)
Authors: Roemer, Jake, Bond, Michael D.
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2964189918