VeriSmart 2.0: SwarmBased BugFinding for Multithreaded Programs with LazyCSeq Swarmbased verification methods split a verification problem into a large number of independent simpler tasks and so exploit the availability of large numbers of cores to speed up verification. LazyCSeq is a BMCbased bugfinding tool for C programs using POSIX threads that is based on sequentialization. Here we present the tool VeriSmart 2.0, which extends LazyCSeq with a swarmbased bugfinding method. The key idea of this approach is to constrain the interleaving such that context switches can only happen within selected tiles (more specifically, contiguous code segments within the individual threads). This underapproximates the programu0027s behaviours, with the number and size of tiles as additional parameters, which allows us to vary the complexity of the tasks. Overall, this significantly improves peak memory consumption and (wallclock) analysis time.

SCMiner: Localizing SystemLevel Concurrency Faults from Large System Call Traces Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intraprocess(threadlevel) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a systemlevel concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failureinducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 realworld benchmarks. The results show that SCMiner is both effective and efficient at localizing systemlevel concurrency faults.