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Sun 20 - Fri 25 October 2019 Athens, Greece
Fri 25 Oct 2019 12:07 - 12:30 at Olympia - Concurrency Chair(s): Sophia Drossopoulou

Data races are a real problem for parallel software, yet hard to detect.
Sound predictive analysis observes a program execution and detects data races
that exist in some other, unobserved execution.
However, existing predictive analyses miss races because they do not
scale to full program executions or do not precisely incorporate data and control dependence.

This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions.
An evaluation using real, large Java programs shows that these approaches
detect more data races than the closest related approaches,
thus advancing the state of the art in sound predictive race detection.

Fri 25 Oct
Times are displayed in time zone: (GMT+03:00) Beirut change

11:00 - 12:30: OOPSLA - Concurrency at Olympia
Chair(s): Sophia DrossopoulouImperial College London
splash-2019-oopsla11:00 - 11:22
Yoav ZurielTechnion - Israel, Michal FriedmanTechnion - Israel, Gali SheffiTechnion - Israel, Nachshon CohenAmazon, Erez PetrankTechnion - Israel
splash-2019-oopsla11:22 - 11:45
Azalea RaadMPI-SWS, Germany, John WickersonImperial College London, Viktor VafeiadisMPI-SWS, Germany
splash-2019-oopsla11:45 - 12:07
Vimuth FernandoUniversity of Illinois at Urbana-Champaign, Keyur JoshiUniversity of Illinois at Urbana-Champaign, Sasa MisailovicUniversity of Illinois at Urbana-Champaign
splash-2019-oopsla12:07 - 12:30
Kaan GençOhio State University, Jake RoemerOhio State University, Yufan XuOhio State University, Michael D. BondOhio State University
DOI Pre-print