research-article

Effective Techniques for Static Race Detection in Java Parallel Loops

Authors:

Danny DigAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 24, Issue 4

Article No.: 24, Pages 1 - 30

https://doi.org/10.1145/2729975

Published: 02 September 2015 Publication History

Abstract

Despite significant progress in recent years, the important problem of static race detection remains open. Previous techniques took a general approach and looked for races by analyzing the effects induced by low-level concurrency constructs (e.g., java.lang.Thread). But constructs and libraries for expressing parallelism at a higher level (e.g., fork-join, futures, parallel loops) are becoming available in all major programming languages. We claim that specializing an analysis to take advantage of the extra semantic information provided by the use of these constructs and libraries improves precision and scalability.

We present IteRace, a set of techniques that are specialized to use the intrinsic thread, safety, and dataflow structure of collections and of the new loop parallelism mechanism introduced in Java 8. Our evaluation shows that IteRace is fast and precise enough to be practical. It scales to programs of hundreds of thousands of lines of code and reports very few race warnings, thus avoiding a common pitfall of static analyses. In five out of the seven case studies, IteRace reported no false warnings. Also, it revealed six bugs in real-world applications. We reported four of them: one had already been fixed, and three were new and the developers confirmed and fixed them.

Furthermore, we evaluate the effect of each specialization technique on the running time and precision of the analysis. For each application, we run the analysis under 32 different configurations. This allows to analyze each technique's effect both alone and in all possible combinations with other techniques.

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Effective Techniques for Static Race Detection in Java Parallel Loops

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 24, Issue 4

Special Issue on ISSTA 2013

August 2015

177 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/2820114

Editor:
David S. Rosenblum
National University of Singapore, Singapore

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Copyright © 2015 ACM.

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Publication History

Published: 02 September 2015

Accepted: 01 January 2015

Revised: 01 January 2015

Received: 01 January 2014

Published in TOSEM Volume 24, Issue 4

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https://doi.org/10.1007/s10766-020-00667-x
Bo LJiang SQian JWang RYao Y(2019)Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical StudyIEEE Access10.1109/ACCESS.2019.29209477(73819-73829)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2920947
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Yu MLee JBae D(2018)AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking PatternsInternational Journal of Parallel Programming10.1007/s10766-018-0579-5Online publication date: 4-Jun-2018
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