Lasso detection using partial-state caching

We study the problem of finding liveness violations in real-world asynchronous and distributed systems. Unlike a safety property, which asserts that certain bad states should never occur during execution, a liveness property states that a program should not remain in a bad state for an infinitely long period of time. Checking for liveness violations is essential to ensure that a system will always make progress in production. The violation of a liveness property can be demonstrated by a finite execution where the same system state repeats twice (known as lasso). However, this requires the ability to capture the state precisely, which is arguably impossible in real-world systems. For this reason, previous approaches have instead relied on demonstrating a long execution where the system remains in a bad state. However, this hampers debugging because the produced trace can be very long, making it hard to understand. Our work aims to find liveness violations in real-world systems while still producing lassos as a bug witness. Our technique relies only on partially caching the system state, which is feasible to achieve efficiently in practice. To make up for imprecision in caching, we use retries: a potential lasso, where the same partial state repeats twice, is replayed multiple times to gain certainty that the execution is indeed stuck in a bad state. We have implemented our technique in the P# programming language and evaluated it on real production systems and several challenging academic benchmarks.