Hybrid dynamic data race detection

We present a new method for dynamically detecting potential data races in multithreaded programs. Our method improves on the state of the art in accuracy, in usability, and in overhead. We improve accuracy by combining previously known race detection techniques -- lockset-based detection and happens-before-based detection -- to obtain fewer false positives than lockset-based detection alone. We enhance usability by reporting more information about detected races than any previous dynamic detector. We reduce overhead compared to previous detectors -- particularly for large applications such as Web application servers -- by not relying on happens-before detection alone, by introducing a new optimization to discard redundant information, and by using a two phase approach to identify error-prone program points and then focus instrumentation on those points. We justify our claims by presenting the results of applying our tool to a range of Java programs, including the widely-used Web application servers Resin and Apache Tomcat. Our paper also presents a formalization of locksetbased and happens-before-based approaches in a common framework, allowing us to prove a folk theorem that happens-before detection reports fewer false positives than lockset-based detection (but can report more false negatives), and to prove that key optimizations are correct.