The actor model is a great tool for various use cases. Though, it’s not the only tool, and sometimes perhaps not even the best. Consequently, developers started mixing and matching high-level concurrency models based on the problem at hand, much like other programming abstractions. Though, this comes with various problems. For instance, we don’t usually have debugging tools that help us to make sense of the resulting system. If we even have a debugger, it may barely allow us to step through our programs instruction by instruction.

Let’s imagine a better world! One were we can follow asynchronous messages, jump to the next transaction commit, or break on the next fork/join task created. Though, race conditions remain notoriously difficult to reproduce. One solutions it to record our program’s execution, ideally capturing the bug. Then we can replay it as often as need to identify the cause of our bug.

The hard bit here is making record & replay practical. I will explain how our concurrency-model-agnostic approach allows us to record model interactions trivially for later replay, and how we minimized its run-time overhead. In the case of actor applications, we can even make the snapshotting fast to be able to limit trace sizes.

Having better debugging capabilities is a real productivity boost. Though, some bugs will always slip through the cracks. So, what if we could prevent those bugs from causing issues? Other researchers have shown how to do it, and I’ll conclude this talk with some ideas on how we can utilize the knowledge we have in our language implementations to make such mitigation approaches fast.

The talk is based on work done in collaboration with Dominik Aumayr, Carmen Torres Lopez, Elisa Gonzalez Boix, and Hanspeter Mössenböck.