# Data Race Prediction for Inaccurate Traces

## TL;DR

This paper introduces diagnostic methods to assess the accuracy of data race predictions from traces, accounting for potential inaccuracies in tracing, and empirically compares these methods with existing race predictors.

## Contribution

It presents novel diagnostic techniques to evaluate the correctness of race predictions under trace inaccuracies, enhancing the reliability of data race detection.

## Key findings

- Diagnostic methods effectively identify guaranteed races.
- Inaccurate traces can lead to false positives in race detection.
- Empirical results show improved reliability over existing predictors.

## Abstract

Happens-before based data race prediction methods infer from a trace of events a partial order to check if one event happens before another event. If two two write events are unordered, they are in a race. We observe that common tracing methods provide no guarantee that the trace order corresponds to an actual program run. The consequence of inaccurate tracing is that results (races) reported are inaccurate. We introduce diagnostic methods to examine if (1) a race is guaranteed to be correct regardless of any potential inaccuracies, (2) maybe is incorrect due to inaccurate tracing. We have fully implemented the approach and provide for an empirical comparison with state of the art happens-before based race predictors such as FastTrack and SHB.