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Conference Paper: CARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications

TitleCARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications
Authors
KeywordsSampling
Data Races
Concurrency
Bug Detection
Issue Date2012
PublisherACM.
Citation
The 2012 International Symposium on Software Testing and Analysis (ISSTA 2012), Minneapolis, MN., 15-20 July 2012. In Proceedings of the ISSTA, 2012, p. 221−231 How to Cite?
AbstractDynamic race detectors can explore multiple thread schedules of a multithreaded program over the same input to detect data races. Although existing sampling-based precise race detectors reduce overheads effectively so that lightweight precise race detection can be performed in testing or post-deployment environments, they are ineffective in detecting races if the sampling rates are low. This paper presents CARISMA to address this problem. CARISMA exploits the insight that along an execution trace, a program may potentially handle many accesses to the memory locations created at the same site for similar purposes. Iterating over multiple execution trials of the same input, CARISMA estimates and distributes the sampling budgets among such location creation sites, and probabilistically collects a fraction of all accesses to the memory locations associated with such sites for subsequent race detection. Our experiment shows that, compared with PACER on the same platform and at the same sampling rate (such as 1%), CARISMA is significantly more effective. © 2012 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/160101
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorZhai, Ken_HK
dc.contributor.authorXu, Ben_HK
dc.contributor.authorChan, WKen_HK
dc.contributor.authorTse, THen_HK
dc.date.accessioned2012-08-16T06:03:11Z-
dc.date.available2012-08-16T06:03:11Z-
dc.date.issued2012en_HK
dc.identifier.citationThe 2012 International Symposium on Software Testing and Analysis (ISSTA 2012), Minneapolis, MN., 15-20 July 2012. In Proceedings of the ISSTA, 2012, p. 221−231en_US
dc.identifier.isbn978-1-4503-1454-1-
dc.identifier.urihttp://hdl.handle.net/10722/160101-
dc.description.abstractDynamic race detectors can explore multiple thread schedules of a multithreaded program over the same input to detect data races. Although existing sampling-based precise race detectors reduce overheads effectively so that lightweight precise race detection can be performed in testing or post-deployment environments, they are ineffective in detecting races if the sampling rates are low. This paper presents CARISMA to address this problem. CARISMA exploits the insight that along an execution trace, a program may potentially handle many accesses to the memory locations created at the same site for similar purposes. Iterating over multiple execution trials of the same input, CARISMA estimates and distributes the sampling budgets among such location creation sites, and probabilistically collects a fraction of all accesses to the memory locations associated with such sites for subsequent race detection. Our experiment shows that, compared with PACER on the same platform and at the same sampling rate (such as 1%), CARISMA is significantly more effective. © 2012 ACM.en_HK
dc.languageengen_US
dc.publisherACM.-
dc.relation.ispartofProceedings of the ISSTA 2012en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectSamplingen_HK
dc.subjectData Racesen_HK
dc.subjectConcurrencyen_HK
dc.subjectBug Detectionen_HK
dc.titleCARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applicationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailZhai, K: kzhai@cs.hku.hken_HK
dc.identifier.emailXu, B: bnxu@cs.hku.hk-
dc.identifier.emailChan, WK: wkchan@cityu.edu.hk-
dc.identifier.emailTse, TH: thtse@cs.hku.hk-
dc.identifier.authorityTse, TH=rp00546en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1145/04000800.2336780en_HK
dc.identifier.scopuseid_2-s2.0-84865306264en_HK
dc.identifier.hkuros203908en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84865306264&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage221en_HK
dc.identifier.epage231en_HK
dc.publisher.placeUnited States-
dc.identifier.scopusauthoridTse, TH=7005496974en_HK
dc.identifier.scopusauthoridChan, WK=23967779900en_HK
dc.identifier.scopusauthoridXu, B=55342365800en_HK
dc.identifier.scopusauthoridZhai, K=36520141500en_HK

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