Dynamic Analyses for Data-Race Detection

RV 2012 Invited Tutorial

John Erickson, Microsoft
Stephen N. Freund, Williams College
Madan Musuvathi, Microsoft


Data races caused by unsynchronized accesses to shared data have long been the source of insidious errors in concurrent software. They are hard to identify during testing, reproduce, and debug. Recent advances in race detection tools show great promise for improving the situation, however, and can enable programmers to find and eliminate race conditions more effectively.

This tutorial explores dynamic analysis techniques to efficiently find data races in large-scale software. It covers the theoretical underpinnings, implementation techniques, and reusable infrastructure used to build state-of-the-art data-race detectors (as well as analyses targeting other types of concurrency errors).

The tutorial provides industrial case studies on finding data races and closes with a discussion of open research questions in this area.

Tutorial Slides

Stephen Freund was supported, in part, by the National Science Foundation under Grants No. 1116825 and 0644130.