Towards concurrency race debugging: an integrated approach for constraint solving and dynamic slicing

Existing data race detectors can witness race conditions for their occurrence but few can also identify the fundamental reasons for their understanding. In this paper, we present a new debugging framework for providing a concise schedule that can help not only witness but also understand data race. The key innovation of this work is to integrate dynamic slicing into constraint solving technique. We propose to use race manifestation (a fraction of memory accesses) to characterize buggy behaviors arising from data races. By encoding race manifestation constraints, existing constraint solving system is enabled to generate the buggy-behavior-exposing schedule, which is further simplified to a minimum as root cause via a tailored data race slicing technique further proposed.

Results show the effectiveness of race manifestation in exposing buggy behaviors. Evaluation on both real-world and benchmark programs shows that root cause schedules can diagnose 376 out of 382 data races detected by the state-of-the-art, with 99.7% fewer events and 99.3% fewer inter-thread context switches compared to the original schedules.