RDIT: race detection from incomplete traces

Abstract

We present RDIT, a novel dynamic algorithm to precisely detect data races in multi-threaded programs with incomplete trace information - the presence of missing events. RDIT enhances the Happens-Before algorithm by relaxing the need to collect the full execution trace, while still being precise and maximal i.e, it detects a maximal set of true data races while generating no false positives. Our approach is based on a sound BarrierPair model that abstracts away missing events by capturing the invocation data of their enclosing methods. By making the least conservative abstraction and by formulating maximal thread causality as logical constraints, we can detect a maximal set of true races from the information available.