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SIFT: A Tool for Property Directed Symbolic Execution of Multithreaded Software

Analyzing multithreaded programs is notoriously hard due to the exponential number of thread interleavings. Although race detectors can help developers find and fix such bugs before the code is deployed, multithreaded code may still be buggy due to memory errors and assertion violations that are not due to race conditions. This paper presents a property directed symbolic execution of multithreaded code. Our approach, named SIFT, differs from previous work on detecting errors in multithreaded code by being property directed and by handling both memory safety and assertion checking that can be further customized by the user. SIFT can detect bugs that may or may not be due to data races, and works in an iterative way. In each step, it explores the state space using selective scheduling based on a set of interleaving points that have been inferred in the previous step. We have developed three partitioning strategies for improved effectiveness and performance. We have implemented SIFT on top of the KLEE symbolic execution engine and applied it to various real-world and academic benchmarks. SIFT could detect more vulnerabilities than a state-of-the-art memory vulnerability detector.