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 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 two key optimizations are correct.

We present a novel approach to dynamic datarace detection for multithreaded object-oriented programs. Past techniques for on-the-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 x to 30 x . 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.

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The Factored Control Flow Graph, FCFG, is a novel representation of a program's intraprocedural control flow, which is designed to efficiently support the analysis of programs written in languages, such as Java, that have frequently occurring operations whose execution may result in exceptional control flow. The FCFG is more compact than traditional CFG representations for exceptional control flow, yet there is no loss of precision in using the FCFG. In this paper, we introduce the FCFG representation and outline how standard forward and backward data flow analysis algorithms can be adapted to work on this representation. We also present empirical measurements of FCFG sizes for a large number of methods obtained from a variety of Java programs, and compare these sizes with those of a traditional CFG representation.

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