MaTSa: Race Detection in Java

Parallel programs are prone to data races, which are concurrency bugs that are difficult to track and reproduce. Various attempts have been made to create or incorporate tools that aim to dynamically detect data races in Java, but most rely on external race detectors that: a) miss some of the nuances in the Java Memory Model (JMM), b) are too slow and complicated to be used in complex real-world applications, or c) produce a lot of false positive reports. In this paper, we present MaTSa, a tool built within OpenJDK, that aims to dynamically detect data races and offer informative pointers to the origin of the race. We evaluate MaTSa and detect several races in the Renaissance benchmark suite and the Quarkus framework, many of which have been reported and resulted in upstream fixes. We compare MaTSa to Java TSan, the only current state-of-the-art dynamic race detector that works on recent OpenJDK versions. We analyze issues with false positives and false negatives for both tools and explain the design decisions causing them. We found MaTSa to be 15x faster on average, while scaling to large programs not supported by other tools.