Race conditions are common in web applications but are difficult to diagnose and repair. Although there exist tools for detecting races in web applications, they all report a large number of false positives. That is, the races they report are either bogus, meaning they can never occur in practice, or benign, meaning they do not lead to erroneous behaviors. Since manually diagnosing them is tedious and error prone, reporting these race warnings to developers would be counter-productive. We propose a platform-agnostic, deterministic replay-based method for identifying not only the real but also the truly harmful race conditions. It relies on executing each pair of racing events in two different orders and assessing their impact on the program state: we say a race is harmful only if (1) both of the two executions arefeasible and (2) they lead to different program states. We have evaluated our evidence-based classification method on a large set of real websites from Fortune-500 companies and demonstrated that it significantly outperforms all state-of-the-art techniques.

We propose a new method for runtime checking of a relaxed consistency property called quasi linearizability for concurrent data structures.Quasi linearizability generalizes the standard notion of linearizability by introducing nondeterminism into the parallel computations quantitatively and then exploiting such nondeterminism to improve the runtime performance. However, ensuring the quantitative aspects of this correctness condition in the low-level code of the concurrent data structure implementation is a difficult task.Our runtime verification method is the first fully automated method for checking quasi linearizability in the C/C++ code of concurrent data structures. It guarantees that all the reported quasi linearizability violations manifested by the concurrent executions are real violations. We have implemented our method in a software tool based on the LLVM compiler and a systematic concurrency testing tool called Inspect. Our experimental evaluation shows that the new method is effective in detecting quasi linearizability violations in the source code implementations of concurrent data structures.

We propose a new method for runtime prevention of type state violations in multithreaded applications due to erroneous thread interleavings. The new method employs a combination of static and dynamic program analysis techniques to control the execution order of the method calls to suppress illegal call sequences. The legal behavior of a shared object is specified by a type-state automaton, which serves as the guidance for our method to delay certain method calls at run time. Our main contribution is a new theoretical framework for ensuring that the runtime prevention strategy is always safe, i.e., they do not introduce new erroneous interleavings. Furthermore, whenever the static program analysis is precise enough, our method guarantees to steer the program to a failurefree interleaving as long as such interleaving exists. We have implemented the new method in a tool based on the LLVM compiler framework. Our experiments on a set of multithreaded C/C++ applications show that the method is both efficient and effective in suppressing concurrency related type-state violations.

We propose a new method for runtime checking of a relaxed consistency property called quasi linearizability for concurrent data structures. Quasi linearizability generalizes the standard notion of linearizability by intentionally introducing nondeterminism into the parallel computations and exploiting such nondeterminism to improve the performance. However, ensuring the quantitative aspects of this correctness condition in the low level code is a difficult task. Our method is the first fully automated method for checking quasi linearizability in the unmodified C/C++ code of concurrent data structures. It guarantees that all the reported quasi linearizability violations are real violations. We have implemented our method in a software tool based on LLVM and a concurrency testing tool called Inspect. Our experimental evaluation shows that the new method is effective in detecting quasi linearizability violations in the source code of concurrent data structures.