This is our second post on hunting data races in real-world applications using RV-Predict. As promised in our previous post, we are now going after the big-name projects! This time our test subject is Apache Tomcat, which is arguably the most popular Java application server at the time being.
This blog post is the beginning of a series of blog posts recording our experience of hunting data races in complex real-world applications using RV-Predict. To start with, we will choose the K framework as our first test subject.
Why the K framework? Well, there are a number of reasons. First of all, it's a very cool project! Check out its website for a quick introduction. It is also the foundation of our RV-Match product, which aims to provide strong correctness guarantees through formal verification. Therefore, it's very important that the underlying K Framework is free from bugs including data races. Finally, the K framework is complex. It is written in a combination of Java and Scala and makes use of the Java 8 features extensively. It also takes advantage of many 3rd-party libraries such as Guice, Guava, Nailgun, etc. As a practical race detector, RV-Predict must be able to handle all these aspects gracefully.
Data races are a common kind of concurrency bug in multithreaded applications. A data race can be defined as two threads accessing a shared memory location concurrently and at least one of the accesses is a write. Data races are notoriously difficult to find and reproduce because they often happen under very specific circumstances. Therefore, you could have a successful pass of the tests most of the time but some test fails once in a while with some mysterious error message far from the root cause of the data race.
Despite all the effort on solving this problem, it remains a challenge in practice to detect data races effectively and efficiently. RV-Predict aims to change this undesired situation. In this blog post, we will summarize some of the most popular kinds of data races in Java and show you how to catch them using RV-Predict. The examples in this post are included in the RV-Predict distribution under the