You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

A Survey on Static Data Race Detection Methods



Research group:

Publication Type:

Report - MRTC


Mälardalen Real-Time Research Centre, Mälardalen University




In this paper, we have conducted a survey on the methods used by various static data race detection tools. Static race detection techniques need to (i) identify what the possible conflicting memory accesses are, and in which pro- gram statements these conflicting accesses occur, (ii) identify the conflicting ac- cesses that are simultaneous, (iii) generate warnings, and (iv) reduce false pos- itives. Analysis of locksets, for programs having lock based synchronization, is an essential component in order to detect if two potential conflicting accesses are simultaneous or not. We have considered the tools Warlock [15], RacerX [1], Chord [8], Relay [17], and the static race detection methods of Kahlon et al. [2, 3]; we have studied how the above four essential requirements are analyzed in the previously mentioned tools. Scalability, soundness, and precision of the tools are compared, and finally classification of race warnings provided by some dy- namic race detection tools are provided. We believe that this classification helps us understand why some race warnings are harmful and why some others are not.


author = {Abu Naser Masud},
title = {A Survey on Static Data Race Detection Methods},
month = {March},
year = {2016},
publisher = {M{\"a}lardalen Real-Time Research Centre, M{\"a}lardalen University},
url = {}