Abstract
AbstractAutomatic generation of parallel unit tests is an efficient and systematic way of identifying data races inside a program. In order to be effective parallel unit tests have to be analysed by race detectors. However, each race detector is suitable for different kinds of race conditions. This leaves the question which race detectors to execute on which unit tests. This paper presents an approach to generate classified parallel unit tests: A class indicates the suitability for race detectors considering low-level race conditions, high-level atomicity violations or race conditions on correlated variables. We introduce a hybrid approach for detecting endangered high-level atomic regions inside the program under test. According to these findings the approach classifies generated unit tests as low-level, atomic high-level or correlated high-level. Our evaluation results confirmed the effectiveness of this approach. We were able to correctly classify 83% of all generated unit tests.KeywordsUnit TestCorrelate VariableComputational UnitRace ConditionAtomic RegionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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