We present new techniques for fast, accurate and scalable static data race detection in
concurrent programs. Focusing our analysis on Linux device drivers allowed us to identify …

Triggering errors in concurrent programs is a notoriously difficult task. A key reason for this is
the behavioral complexity resulting from the large number of interleavings of operations of …

Dynamic race detection is the problem of determining if an observed program execution
reveals the presence of a data race in a program. The classical approach to solving this …

Manually detecting bugs in concurrent programs is hard due to the myriad of thread
interleavings that needs to be accounted for. Higher-order programming features only …

We propose a static analysis framework for concurrent programs based on reduction of
thread interleavings using sound invariants on the top of partial order techniques. Starting …

We introduce a generic approach for monitoring multithreaded programs online leveraging
existing runtime verification (RV) techniques. In our setting, monitors are deployed to monitor …

We present maximal causality reduction (MCR), a new technique for stateless model
checking. MCR systematically explores the state-space of concurrent programs with a …

Concurrent programs are notorious for containing errors that are difficult to reproduce and
diagnose. Two common kinds of concurrency errors are data races and atomicity violations …

A runtime monitor checks a safety property during a program's execution. A parameterized
runtime monitor can monitor properties containing free variables, or parameters. For …

Data races are subtle and difficult to detect errors that arise during concurrent program
execution. Traditional testing techniques fail to find these errors, but recent research has …