research-article

Data races vs. data race bugs: telling the difference with portend

Authors:

Cristian Zamfir,

George CandeaAuthors Info & Claims

ACM SIGPLAN Notices, Volume 47, Issue 4

Pages 185 - 198

https://doi.org/10.1145/2248487.2150997

Published: 03 March 2012 Publication History

Abstract

Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Alas, spotting just the harmful data races in programs is like finding a needle in a haystack: 76%-90% of the true data races reported by state-of-the-art race detectors turn out to be harmless [45]. We present Portend, a tool that not only detects races but also automatically classifies them based on their potential consequences: Could they lead to crashes or hangs? Could their effects be visible outside the program? Are they harmless? Our proposed technique achieves high accuracy by efficiently analyzing multiple paths and multiple thread schedules in combination, and by performing symbolic comparison between program outputs.

We ran Portend on 7 real-world applications: it detected 93 true data races and correctly classified 92 of them, with no human effort. 6 of them are harmful races. Portend's classification accuracy is up to 88% higher than that of existing tools, and it produces easy-to-understand evidence of the consequences of harmful races, thus both proving their harmfulness and making debugging easier. We envision Portend being used for testing and debugging, as well as for automatically triaging bug reports.

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Index Terms

Data races vs. data race bugs: telling the difference with portend
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

Recommendations

Data races vs. data race bugs: telling the difference with portend
ASPLOS '12

Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Alas, spotting just the harmful data races in programs is like finding a needle in a haystack: 76%-90% of the true data races reported by ...
Data races vs. data race bugs: telling the difference with portend
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Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Alas, spotting just the harmful data races in programs is like finding a needle in a haystack: 76%-90% of the true data races reported by ...
Automated Classification of Data Races Under Both Strong and Weak Memory Models

Data races are one of the main causes of concurrency problems in multithreaded programs. Whether all data races are bad, or some are harmful and others are harmless, is still the subject of vigorous scientific debate [Narayanasamy et al. 2007; Boehm 2012]...

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 47, Issue 4

ASPLOS '12

April 2012

453 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2248487

Issue’s Table of Contents

ASPLOS XVII: Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
March 2012
476 pages
ISBN:9781450307598
DOI:10.1145/2150976
General Chair:
Tim Harris
Microsoft Research
,
Program Chair:
Michael L. Scott
University of Rochester

Copyright © 2012 ACM.

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Published: 03 March 2012

Published in SIGPLAN Volume 47, Issue 4

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Niskov FKutovoy EKurmangaleev S(2023)Enhanced S2E for Analysis of Multi-Thread SoftwareProgramming and Computing Software10.1134/S036176882309007449:Suppl 1(S39-S44)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1134/S0361768823090074
Ma LZhou DWu HZhou YChang RXiong HWu LRen K(2023)When Top-down Meets Bottom-up: Detecting and Exploiting Use-After-Cleanup Bugs in Linux Kernel2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179356(2138-2154)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179356
Jain RPurandare RSharma S(2022)BiRD: Race Detection in Software Binaries under Relaxed Memory ModelsACM Transactions on Software Engineering and Methodology10.1145/349853831:4(1-29)Online publication date: 12-Jul-2022
https://dl.acm.org/doi/10.1145/3498538
Lu JLi FLiu CLi LFeng XXue J(2022)CloudRaid: Detecting Distributed Concurrency Bugs via Log Mining and EnhancementIEEE Transactions on Software Engineering10.1109/TSE.2020.299936448:2(662-677)Online publication date: 1-Feb-2022
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