Writing reliable concurrent software remains a huge challenge for today's programmers.
Programmers rarely reason about their code by explicitly considering different possible inter …

Data races are among the most reliable indicators of programming errors in concurrent
software. For at least two decades, Lamport's happens-before (HB) relation has served as …

Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism
introduces subtle errors that are both hard to detect and to reproduce. The most common …

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 …

Writing concurrent programs is highly error-prone due to the nondeterminism in interprocess
communication. The most reliable indicators of errors in concurrency are data races, which …

Despite the numerous static and dynamic program analysis techniques in the literature, data
races remain one of the most common bugs in modern concurrent software. Further, the …

We present RDIT, a novel dynamic technique to detect data races in multithreaded programs
with incomplete trace information, ie, in the presence of missing events. RDIT is both precise …

Data races are a real problem for parallel software, yet hard to detect. Sound predictive
analysis observes a program execution and detects data races that exist in some other …

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 …

Happens before-based dynamic analysis is the go-to technique for detecting data races in
large scale software projects due to the absence of false positive reports. However, such …