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RacerX: effective, static detection of race conditions and deadlocks
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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Cited by
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
A survey of symbolic execution techniques
Baldoni, R., Coppa, E., D’elia, D. C., Demetrescu, C., & Finocchi, I. (2019) · ACM Computing Surveys · doi:10.1145/3182657 · Cited by 708
ThreadSanitizer: data race detection in practice
Serebryany, K., & Iskhodzhanov, T. (2009) · Proceedings of the Workshop on Binary Instrumentation and Applications · doi:10.1145/1791194.1791203 · Cited by 451
Large Language Models for Test-Free Fault Localization
Yang, A. Z. H., Le Goues, C., Martins, R., & Hellendoorn, V. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3623342 · Cited by 144
Metadata
- Authors
- Engler, D., & Ashcraft, K.
- Venue
- ACM SIGOPS Operating Systems Review
- Published
- 2003-10-19
- Volume
- 37
- Issue
- 5
- Pages
- 237-252
- ISSN
- 0163-5980 (ISSN-L)
- OpenAlex
- W2149984854
- Cited by
- 661
- References
- 0
- FWCI
- 65.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Engler, D., & Ashcraft, K. (2003). RacerX: effective, static detection of race conditions and deadlocks. ACM SIGOPS Operating Systems Review. https://doi.org/10.1145/1165389.945468
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