There exist no sound, scalable methods to assemble comprehensive datasets of concurrent programs annotated with data races.
As a consequence, it is unclear how well the multiple heuristics and SMT-based algorithms, that have been proposed over the last three decades to detect data races, perform.
To address this problem, we propose \sys—an SMT-based approach which, for any given program, creates arbitrarily many program traces of it containing injected data races.
The injected races are guaranteed to follow the given program's semantics.
{\sys} hence can produce an arbitrarily large, labeled benchmark which is independent of how detection algorithms work.
We demonstrate {\sys} by injecting races into popular program benchmarks and generating a small dataset of traces with races in them.
Among the traces {\sys} generates, we begin to find counterexamples which four state-of-the-art race detection algorithms fail to detect.
We thus demonstrate the utility of generating such datasets, and recommend using them to train machine learning-based models which can potentially replace and improve upon existing race-detection heuristics.

Sat 17 Jun

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:50
SOAP: Session 4 - Program Verification and Dynamic AnalysisSOAP at Magnolia 18
Chair(s): Liana Hadarean Amazon Web Services

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16:00
35m
Keynote
Applications of Symbolic ExecutionInvited Talk
SOAP
William Hallahan Binghamton
16:35
25m
Talk
Completeness Thresholds for Memory Safety of Array Traversing Programs
SOAP
Tobias Reinhard KU Leuven, Justus Fasse KU Leuven, Bart Jacobs KU Leuven
DOI
17:00
25m
Talk
Crosys: Cross Architectural Dynamic Analysis
SOAP
Sangrok Lee The Affiliated Institute of ETRI, Jieun Lee The Affiliated Institute of ETRI, Jaeyong Ko The Affiliated Institute of ETRI, Jaewoo Shim The Affiliated Institute of ETRI
DOI
17:25
25m
Talk
RaceInjector: Injecting Races to Evaluate and Learn Dynamic Race Detection Algorithms (Virtual)
SOAP
Michael Wang Massachusetts Institute of Technology, Shashank Srikant MIT, Malavika Samak CSAIL, MIT, Una-May O’Reilly Massachusetts Institute of Technology
DOI