We present a novel approach to dynamic datarace detection for multithreaded object-oriented programs. Past techniques for onthe -fly datarace detection either sacrificed precision for performance, leading to many false positive datarace reports, or maintained precision but incurred significant overheads in the range of 3# to 30#. In contrast, our approach results in very few false positives and runtime overhead in the 13% to 42% range, making it both efficient and precise. This performance improvement is the result of a unique combination of complementary static and dynamic optimization techniques.

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 two 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 lockset-based 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 two key optimizations are correct.

Multithreaded shared-memory programs are susceptible to dataraces, bugs that may exhibit themselves only in rare circumstances and can have detrimental effects on program behavior. Dataraces are often difficult to debug because they are difficult to reproduce and can affect program behavior in subtle ways, so tools which aid in detecting and preventing dataraces can be invaluable. Past dynamic datarace detection tools either incurred large overhead, ranging from 3x to 30x, or sacrificed precision in reducing overhead, reporting many false errors. This thesis presents a novel approach to efficient and precise datarace detection for multithreaded objectoriented programs. Our runtime datarace detector incurs an overhead ranging from 13 % to 42 % for our test suite, well below the overheads reported in previous work. Furthermore, our precise approach reveals dangerous dataraces in real programs with few spurious warnings.