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RacerX: effective, static detection of race conditions and deadlocks

Engler, Dawson, Ashcraft, Ken·2003·Proceedings of the nineteenth ACM symposium on Operating systems principles

Abstract

This paper describes RacerX, a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It is explicitly designed to find errors in large, complex multithreaded systems. It aggressively infers checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. It tracks a set of code features which it uses to sort errors both from most to least severe. It uses novel techniques to counter the impact of analysis mistakes. The tool is fast, requiring between 2-14 minutes to analyze a 1.8 million line system. We have applied it to Linux, FreeBSD, and a large commercial code base, finding serious errors in all of them. RacerX is a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It uses novel strategies to infer checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. We applied it to FreeBSD, Linux and a large commercial code base and found serious errors in all of them.

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Metadata

Authors
Engler, D., & Ashcraft, K.
Venue
Proceedings of the nineteenth ACM symposium on Operating systems principles
Publisher
ACM
Published
2003-10-19
Pages
237-252
OpenAlex
W4251803824
Cited by
368
References
0
FWCI
9.57
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: RacerX: effective, static detection of race conditions and deadlocks. https://app.cochat.ai/papers/10.1145/945445.945468
The paper
Engler, D., & Ashcraft, K. (2003). RacerX: effective, static detection of race conditions and deadlocks. Proceedings of the nineteenth ACM symposium on Operating systems principles. https://doi.org/10.1145/945445.945468

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.